Automated defect inspection of LED chip using deep convolutional neural network
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Existing deep learning-based industrial defect detection methods do not specifically optimize the model's ability to extract and fuse features, which is crucial for defect detection.
“However, the above deep learning-based industrial defect detection methods do not specifically optimize the model's ability to extract and fuse features, which is crucial for defect detection.”
From A deep learning model for steel surface defect detection (2024) · cites this paper as [18]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 43,748 · see citing works
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · see citing works
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599 · see citing works
Learning Deep Features for Discriminative Localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.319 · Cited by 11,428 · see citing works
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382 · see citing works
Computer vision: algorithms and applications
Szeliski, R. (2011) · Choice Reviews Online · doi:10.5860/choice.48-5140 · Cited by 4,007 · see citing works
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6229 · Cited by 2,680 · see citing works
Schubert, E. F., Cho, J., & Kim, J. K. (2006) · Kirk-Othmer Encyclopedia of Chemical Technology · doi:10.1002/0471238961.1209070811091908.a01.pub3 · Cited by 2,021 · see citing works
IEEE Transactions on Systems, Man, and Cybernetics
Institute of Electrical and Electronics Engineers (2021) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.21 · Cited by 1,286 · see citing works
Schubert, E. F., Gessmann, T., & Kim, J. K. (2005) · Kirk-Othmer Encyclopedia of Chemical Technology · doi:10.1002/0471238961.1209070811091908.a01.pub2 · Cited by 1,250 · see citing works
Light-Emitting Diodes by E. Fred Schubert
Schubert, E. F. (2006) · Cited by 1,171 · see citing works
Light-Emitting Diodes: Light-Emitting Diodes
Schubert, E. F. (2006) · Cited by 1,169 · see citing works
Computer-Vision-Based Fabric Defect Detection: A Survey
Kumar, A. (2008) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.1930.896476 · Cited by 725 · see citing works
Wu, C., Wu, T., Tsai, J., et al. (2008) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2008.921206 · Cited by 113 · see citing works
Design and Implementation of RGB LED Drivers for LCD Backlight Modules
Lo, Y., Wu, K., Pai, K., et al. (2009) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2009.2026771 · Cited by 110 · see citing works
A Shift-Tolerant Dissimilarity Measure for Surface Defect Detection
Tsai, D., Chiang, I.-Y., & Tsai, Y.-H. (2011) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2011.2166797 · Cited by 83 · see citing works
Lin, C. (2006) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2006.885143 · Cited by 59 · see citing works
Series–Parallel Autoregulated Charge-Balancing Rectifier for Multioutput Light-Emitting Diode Driver
Wu, X., Hu, C., Zhang, J., et al. (2013) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2013.2257150 · Cited by 52 · see citing works
Automatic inspection system of LED chip using two-stages back-propagation neural network
Kuo, C. J., Hsu, C.-T. M., Liu, Z., & Wu, H. (2013) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-012-0725-7 · Cited by 51 · see citing works
LEDs in Real Lighting Applications: from Niche Markets to General Lighting
Wendt, M., & Andriesse, J.-W. (2006) · Conference record · doi:10.1109/ias.2006.256905 · Cited by 50 · see citing works
Blob analyzation-based template matching algorithm for LED chip localization
Zhong, F., He, S., & Li, B. (2015) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-015-7638-5 · Cited by 47 · see citing works
Self-citationLin, H.-D. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.09.014 · Cited by 34 · see citing works
Self-citationVisual Stability Prediction and Its Application to ManipulationPreprint
Li, W., Leonardis, A., & Fritz, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.04861 · Cited by 27 · see citing works
Development of an Automatic Stencil Inspection System Using Modified Hough Transform and Fuzzy Logic
Choi, K.-J., Lee, Y., Moon, J.-W., et al. (2007) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2006.885494 · Cited by 26 · see citing works
Learning Vector Quantization Neural Networks for LED Wafer Defect Inspection
Chang, C., Chang, C.-H., Li, C.-H., et al. (2007) · doi:10.1109/icicic.2007.368 · Cited by 25 · see citing works
Inorganic Semiconductors for Light‐emitting Diodes
Schubert, E. F., Gessmann, T., & Kim, J. K. (2005) · doi:10.1002/3527607986.ch1 · Cited by 17 · see citing works
İmamoğlu, N., Kimura, M., Miyamoto, H., et al. (2017) · arXiv (Cornell University) · Cited by 2 · see citing works
Flagged first, then by citations
1 of the 34 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Segmentation-based deep-learning approach for surface-defect detection
Tabernik, D., Šela, S., Skvarč, J., & Skočaj, D. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01476-x · Cited by 996 · see citing works
“The proposed LEDNet showed a significant improvement in the defect-detection rate compared to traditional methods.”
InfluentialDeep Metallic Surface Defect Detection: The New Benchmark and Detection Network
Lv, X., Duan, F., Jiang, J.-J., Fu, X., & Gan, L. (2020) · Sensors · doi:10.3390/s20061562 · Cited by 591 · see citing works
“Lin et al. [33] proposed a LEDNet to exploit image-annotation and large batch sizes.”
BackgroundAutomatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks
Tao, X., Zhang, D., Ma, W., Liu, X., & Xu, D. (2018) · Applied Sciences · doi:10.3390/app8091575 · Cited by 471 · see citing works
“[34] built a convolutional neural network (CNN) for light emitting diode (LED) chip defect inspection.”
BackgroundVisual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY
Czimmermann, T., Ciuti, G., Milazzo, M., Chiurazzi, M., Roccella, S., Oddo, C. M., & Dario, P. (2020) · Sensors · doi:10.3390/s20051459 · Cited by 441 · see citing works
“Lin, Hui et al. [214] developed a CNN called LEDNet to detect and classify defect on LED chips, where they achieved relatively low inaccuracy of 5.05%.”
BackgroundMethodInfluentialMixed supervision for surface-defect detection: From weakly to fully supervised learning
Božič, J., Tabernik, D., & Skočaj, D. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103459 · Cited by 377 · see citing works
“Fully supervised defect detection Several related works explored the use of deep-learning for industrial anomaly detection and categorisation [24, 18, 13, 42, 35, 27, 17, 38, 14], including the early work of Masci et al.”
InfluentialCheng, X., & Yu, J. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3040485 · Cited by 359 · see citing works
“Lin et al. [9] proposed a class activation mapping technique in a convolutional neural network (CNN) for light-emitting diode (LED) chip defect inspection to localize defects without using region-level human annotations.”
BackgroundImage-Based Crack Detection Methods: A Review
Munawar, H. S., Hammad, A. W. A., HADDAD, A. N., Soares, C. A. P., & Waller, S. T. (2021) · Infrastructures · doi:10.3390/infrastructures6080115 · Cited by 321 · see citing works
Anomaly detection of defects on concrete structures with the convolutional autoencoder
Chow, J. K., Su, Z., Wu, J., Tan, P. S., Mao, X., & Wang, Y. H. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101105 · Cited by 336 · see citing works
Ebayyeh, A. A. R. M. A., & Mousavi, A. (2020) · IEEE Access · doi:10.1109/access.2020.3029127 · Cited by 314 · see citing works
“in [47] used CNN (called LEDNet) for inspecting LED chips defects.”
BackgroundMethodAutomated surface defect detection framework using machine vision and convolutional neural networks
Singh, S. A., & Desai, K. A. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01878-w · Cited by 304 · see citing works
“A CNN-based inspector LEDNet is conceived for quality inspection of LED chips in electronics industries (Lin et al., 2019).”
MethodRecent advances in surface defect inspection of industrial products using deep learning techniques
Zheng, X., Zheng, S., Kong, Y., & Chen, J. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-06592-8 · Cited by 287 · see citing works
A Review on Recent Advances in Vision-based Defect Recognition towards Industrial Intelligence
Gao, Y., Li, X., Wang, X. V., Wang, L., & Gao, L. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.05.008 · Cited by 261 · see citing works
Self-citationKhanam, R., Hussain, M., Hill, R. C., & Allen, P. (2024) · IEEE Access · doi:10.1109/access.2024.3425166 · Cited by 273 · see citing works
“…[347], [348], Optical fiber defect detection [349], [350], Wheel hub surface defect detection [351], [352], [353], Diode chip defect detection [354], [355], [356], Bottle mouth defect detection [357], [358], [359], Precision parts defect detection [360], Varistor defect detection [361],…”
BackgroundPenumuru, D. P., Muthuswamy, S., & Premkumar, K. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01508-6 · Cited by 219 · see citing works
Solar cell surface defect inspection based on multispectral convolutional neural network
Chen, H., Pang, Y., Hu, Q., & Liu, K. (2018) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-018-1458-z · Cited by 203 · see citing works
“lgorithm based on transfer learning. The paper transferred the weight parameters of other models to the current defect detection model to achieve sharing of weights and easing overfitting. Lin et al. [29] first apply a convolutional neural network to the LED surface and realized the identification and positioning of v…”
A deep learning model for steel surface defect detection
Li, Z., Wei, X., Hassaballah, M., Li, Y., & Jiang, X. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01180-7 · Cited by 122 · see citing works
SMD LED chips defect detection using a YOLOv3-dense model
Chen, S., & Tsai, C.-C. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101255 · Cited by 108 · see citing works
Davtalab, O., Kazemian, A., Yuan, X., & Khoshnevis, B. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01684-w · Cited by 106 · see citing works
“In specific, Deep Convolutional Neural Networks (CNN) have been successfully applied to various computer vision problems (Baslamisli et al. 2018; Liu et al. 2018; Jie et al. 2018; Badmos et al. 2020; Lin and Li 2019).”
MethodLee, J., Lee, Y. C., & Kim, J. T. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.10.013 · Cited by 106 · see citing works
Modified DenseNet for Automatic Fabric Defect Detection With Edge Computing for Minimizing Latency
Zhu, Z., Han, G., Jia, G., & Shu, L. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2983050 · Cited by 100 · see citing works
“…multibox detector to extract image features, and subsequently used a deep residual network to classify defects on a steel surface; Jing et al. [35] proposed a detection method for automatic fabric defect detection using a deep CNN, and Lin et al. [36] applied a CNN to LED chip defect inspection.”
MethodA visual long-short-term memory based integrated CNN model for fabric defect image classification
Zhao, Y., Hao, K., He, H., Tang, X., & Wei, B. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.10.067 · Cited by 97 · see citing works
Sarkar, K., Shiuly, A., & Dhal, K. G. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.134212 · Cited by 98 · see citing works
Zhang, J., Su, H., Zou, W., Gong, X., Zhang, Z., & Shen, F. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107571 · Cited by 94 · see citing works
Data-driven prognostic method based on self-supervised learning approaches for fault detection
Wang, T., Qiao, M., Zhang, M., et al. (2018) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-018-1431-x · Cited by 89 · see citing works
“A potential method for product quality inspection can be adopted by analyzing the status of the monitoring data (Lin et al. 2018).”
MethodSingh, S. A., Kumar, A. S., & Desai, K. A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119623 · Cited by 86 · see citing works
Digital Twin of Intelligent Small Surface Defect Detection with Cyber-manufacturing Systems
Wu, Y., Cao, H., Yang, G., Lü, T., & Wan, S. (2022) · ACM Transactions on Internet Technology · doi:10.1145/3571734 · Cited by 75 · see citing works
“Afterwards, Lin et al. [21] propose LEDNet for defect detection on LED chips with 30,000 low-resolution images, where their network follows general structure of AlexNet by replacing fully connected layers with incorporates class-activation maps.”
End-to-end training of a two-stage neural network for defect detection
Božič, J., Tabernik, D., & Skočaj, D. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412092 · Cited by 77 · see citing works
“0 paradigm, newer deep-learning based algorithms have started being employed [11], [7], [5], [9].”
BackgroundMethodLee, K. B., & Kim, C. O. (2018) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-018-1437-4 · Cited by 72 · see citing works
Jin, C., Kim, H.-J., Piao, Y., Li, M., & Piao, M. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01540-x · Cited by 74 · see citing works
On-line part deformation prediction based on deep learning
Zhao, Z., Li, Y., Liu, C., et al. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01465-0 · Cited by 70 · see citing works
Depth-wise Squeeze and Excitation Block-based Efficient-Unet model for surface defect detection
Üzen, H., Türkoğlu, M., Aslan, M., & Hanbay, D. (2022) · The Visual Computer · doi:10.1007/s00371-022-02442-0 · Cited by 70 · see citing works
“Similarly, Lin et al. [61] proposed a LEDNet network for checking LED chips.”
BackgroundA Weakly Supervised Surface Defect Detection Based on Convolutional Neural Network
Liang, X., Lv, S., Deng, Y., & Li, X. (2020) · IEEE Access · doi:10.1109/access.2020.2977821 · Cited by 69 · see citing works
“Lin et al. [33] used a class activation map (CAM) to localize defect regions in the construction of a CNN model for LED chip inspection.”
BackgroundMethodGong, Y., Luo, J. T., Shao, H., & Zhi-xue, L. (2022) · Composite Structures · doi:10.1016/j.compstruct.2021.115136 · Cited by 69 · see citing works
FDD: a deep learning–based steel defect detectors
Akhyar, F., Liu, Y., Hsu, C.-Y., Shih, T. K., & Lin, C. (2023) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-023-11087-9 · Cited by 70 · see citing works
Attention-Guided Multitask Learning for Surface Defect Identification
Sampath, V., Maurtua, I., Martín, J. J. A., Rivera, A., Molina, J., & Gutierrez, A. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3234030 · Cited by 66 · see citing works
“Lin et al. [17] employed a class activation mapping technique in CNN to simultaneously achieve defect classification and localization tasks in the LED chip defect inspection process.”
MethodArtificial intelligence-empowered pipeline for image-based inspection of concrete structures
Chow, J. K., Su, Z., Wu, J., Li, Z., Tan, P. S., Liu, K.-F., Mao, X., & Wang, Y.-H. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103372 · Cited by 65 · see citing works
A Review on Industrial Surface Defect Detection Based on Deep Learning Technology
Qi, S., Yang, J., & Zhong, Z. (2020) · International Conference on Machine Learning and Machine Intelligence (MLMI) · doi:10.1145/3426826.3426832 · Cited by 72 · see citing works
“…the current relevant literature, deep learning has also been applied to electronic component defect inspection [41], diode chip defect inspection [42], optical fiber defect inspection [43], metal surface defect inspection [44], wheel hub surface defect inspection [45], bottle mouth defect…”
BackgroundXiao, M., Yang, B. Y., Wang, S., Zhang, Z., Tang, X., & Kang, L. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103583 · Cited by 63 · see citing works
A data-driven method for enhancing the image-based automatic inspection of IC wire bonding defects
Chen, J., Zhang, Z., & Wu, F. (2020) · International Journal of Production Research · doi:10.1080/00207543.2020.1821928 · Cited by 62 · see citing works
Hsu, C.-Y., & Chien, J.-C. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01687-7 · Cited by 61 · see citing works
A robust weakly supervised learning of deep Conv-Nets for surface defect inspection
Chen, H., Hu, Q., Zhai, B., Chen, H., & Liu, K. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04819-5 · Cited by 58 · see citing works
Automated detection of defects with low semantic information in X-ray images based on deep learning
Du, W., Shen, H., Fu, J., Zhang, G., Shi, X., & He, Q. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01566-1 · Cited by 58 · see citing works
Meng, S., Pan, R., Gao, W., Zhou, J. H., Wang, J., & He, W. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01607-9 · Cited by 57 · see citing works
Leveraging computer vision towards high-efficiency autonomous industrial facilities
Yousif, I., Burns, L., Kalach, F. E., & Harik, R. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02396-1 · Cited by 57 · see citing works
“This reduces the need to stop the production line and does not require precise positioning, which is beneficial for inspection of parts fabricated from soft materials (Lin, 2019).”
Li, C., Zhang, X., Huang, Y., Tang, C., & Fatikow, S. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106530 · Cited by 54 · see citing works
PCB Defect Detection Using Deep Learning Methods
Wu, X., Ge, Y., Zhang, Q., et al. (2021) · IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd49262.2021.9437846 · Cited by 53 · see citing works
“Lin [10] et al. introduced the application of reel neural network in the detection of LED chip defects and realized the detection and location of it.”
BackgroundKim, M., Lee, M., An, M., & Lee, H.-C. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01502-y · Cited by 53 · see citing works
Attention-based deep learning for chip-surface-defect detection
Wang, S., Wang, H., Yang, F., Liu, F., & Zeng, L. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-09425-4 · Cited by 52 · see citing works
“Lin et al. [30] implemented data expansion by geometric transformation, which provided more samples for training.”
BackgroundA Generic Automated Surface Defect Detection Based on a Bilinear Model
Zhou, F., Liu, G., Xu, F., & Deng, H. (2019) · Applied Sciences · doi:10.3390/app9153159 · Cited by 47 · see citing works
VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning
Choi, J., Yi, K. M., Kim, J., Choo, J., Kim, B., Chang, J.-Y., Gwon, Y., & Chang, H. (2020) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr46437.2021.00668 · Cited by 55 · see citing works
“Active learning focuses on efficient labelling of data and has drawn much interest lately [38,41,48], due to deep learning being attempted at new domains, such as biomedical imaging [3, 16] and industrial imaging [27, 49], where ac-”
BackgroundXu, C., & Zhu, G. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01570-5 · Cited by 45 · see citing works
“In the era of industrial intelligent manufacturing, automation (Du et al. 2020; Jin et al. 2020; Kim et al. 2019; Lin et al. 2019) and big data are two important basic information.”
BackgroundDLSE-Net: A robust weakly supervised network for fabric defect detection
Liu, Z., Huo, Z., Li, C., et al. (2021) · Displays · doi:10.1016/j.displa.2021.102008 · Cited by 45 · see citing works
Self-citationMao, Y., Lin, H. Z., Yu, C. X., et al. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-022-02039-3 · Cited by 44 · see citing works
“Deep learning has become the AI/ML technique of choice in the last few years, due to its groundbreaking success in numerous real-world applications (Aghazadeh & Gharehchopogh, 2018; Asghari et al., 2021; Collobert & Weston, 2008;KhataeiMaragheh et al., 2022;Krizhevsky et al., 2017; Lin et al., 2019; Niu et al., 2019).”
BackgroundSelf-citationIntelligent inventory management with autonomation and service strategy
Dey, B. K., & Seok, H. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-022-02046-4 · Cited by 43 · see citing works
“A machine-based autonomated inspection policy was very much effective in detecting defective products (Lin et al., 2019; Sarkar et al., 2020).”
Cognitive intelligence in industrial robots and manufacturing
Mukherjee, A., Divya, A., Sivvani, M., & Pal, S. K. (2024) · Computers & Industrial Engineering · doi:10.1016/j.cie.2024.110106 · Cited by 41 · see citing works
Detecting and Measuring Defects in Wafer Die Using GAN and YOLOv3
Chen, S., Kang, C.-H., & Perng, D. (2020) · Applied Sciences · doi:10.3390/app10238725 · Cited by 41 · see citing works
“[11] designed a six-layer CNN to classify LED chip defects, and used class activation mapping (CAM) to create a heat map corresponding to the analyzed image to locate the defective area.”
MethodDefectGAN: Weakly-Supervised Defect Detection using Generative Adversarial Network
Niu, S., Lin, H. Z., Niu, T., Li, B., & Wang, X. (2019) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2019.8843204 · Cited by 40 · see citing works
“[1] proposed LEDNet combined with CAM, which learns from images labeled with four categories including three types of defects and positive chip.”
Self-citationAn End-to-End Defect Detection Method for Mobile Phone Light Guide Plate via Multitask Learning
Li, Y., & Li, J. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3053987 · Cited by 41 · see citing works
“…of manually designing features [such as support vector machine (SVM), decision tree, or K-nearest neighbor (KNN)], deep convolutional neural networks (DCNNs) have more powerful feature extraction capabilities, and deep learning methods are increasingly used in surface defect detection field [4]–[8].”
MethodTabernik, D., Šuc, M., & Skočaj, D. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.133582 · Cited by 39 · see citing works
“On the other hand, supervised binary segmentation models have been successfully deployed for surface crack detection on concrete [4, 5, 7] and various other construction materials [8–11] as well as to other materials, such as steel [12].”
BackgroundFully convolutional networks for chip-wise defect detection employing photoluminescence images
Stern, M. L., & Schellenberger, M. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01563-4 · Cited by 38 · see citing works
“…et al. (2010) outline several methods to evaluate the quality of multi-crystalline silicon solar cells using photoluminescence measurements, while Lin et al. (2018) report on a convolutional neural network that distinguishes two different defect types in single LED chip images, where the defect…”
MethodA Graph Guided Convolutional Neural Network for Surface Defect Recognition
Wang, Y., Gao, L., Gao, Y., & Li, X. (2022) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2022.3140784 · Cited by 36 · see citing works
“For example, Lin et al. [14] utilized CNN to inspect the defects in LED chips automatically.”
MethodA dynamic inference network (DI-Net) for online fabric defect detection in smart manufacturing
Zhao, S., Zhong, R. Y., Xu, C., Wang, J., & Zhang, J. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02387-2 · Cited by 35 · see citing works
ARMBench: An Object-centric Benchmark Dataset for Robotic Manipulation
Mitash, C., Wang, F., Lu, S., Terhuja, V., Garaas, T. W., Polido, F., & Nambi, M. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160846 · Cited by 34 · see citing works
“defects for individual objects such as LED chips[44], fabrics [58], and metals [6].”
BackgroundChen, A., Li, X., Jing, H., et al. (2023) · Energies · doi:10.3390/en16041619 · Cited by 33 · see citing works
Nahar, L., Awrangjeb, M., & Islam, M. S. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.104067 · Cited by 38 · see citing works
Height consistency compensation in laser-directed energy deposition of thin-walled parts
Ning, J., Zhao, Y., Zhu, L., et al. (2024) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2024.108963 · Cited by 32 · see citing works
Conductive particle detection via deep learning for ACF bonding in TFT-LCD manufacturing
Liu, E.-Y., Chen, K.-P., Xiang, Z., & Zhang, J. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01494-9 · Cited by 28 · see citing works
“The deep learning model is also deployed in many other applications of industry manufacturing (Zhao et al. 2019; Wang et al. 2018; Zan et al. 2019; Lin et al. 2018).”
MethodPurushothaman, K., & Ahmad, R. (2022) · International Journal of Lean Six Sigma · doi:10.1108/ijlss-05-2021-0088 · Cited by 30 · see citing works
Lin, W.-J., Chen, J., Jhuang, J.-P., Tsai, M.-S., Hung, C., Li, K., & Young, H. (2021) · Scientific Reports · doi:10.1038/s41598-021-97610-y · Cited by 29 · see citing works
Multi-scale GAN with transformer for surface defect inspection of IC metal packages
Chen, K., Cai, N., Wu, Z., Xia, H., Zhou, S., & Wang, H. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118788 · Cited by 29 · see citing works
MaMiNet: Memory-attended multi-inference network for surface-defect detection
Luo, X., Li, S., Wang, Y., Zhan, T., Shi, X., & Liu, B. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103834 · Cited by 29 · see citing works
Texture surface defect detection of plastic relays with an enhanced feature pyramid network
Huang, F. C., Wang, B., Li, Q., & Zou, J. T. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01864-2 · Cited by 29 · see citing works
Rački, D., Tomaževič, D., & Skočaj, D. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06397-6 · Cited by 28 · see citing works
“Proposed usage of deep-learning-based solutions for industrial anomaly detection and classification [31, 38, 40, 41, 41–46] includes a shallow network for steel defect classification in early work of [23] and a somewhat comprehensive study of an architecture in [16].”
BackgroundYOLO-Xray: A Bubble Defect Detection Algorithm for Chip X-ray Images Based on Improved YOLOv5
Wang, J., Lin, B., Li, G., et al. (2023) · Electronics · doi:10.3390/electronics12143060 · Cited by 27 · see citing works
Using convolutional neural network for intelligent SAM inspection of flip chips
Wang, W., Lu, X., He, Z., & Shi, T. (2021) · Measurement Science and Technology · doi:10.1088/1361-6501/ac1615 · Cited by 26 · see citing works
“Lin et al [18] designed a CNN based defect inspector named LEDNet to classify between normal and defective chips (with line blem-ishes or scratch marks).”
A Robust Completed Local Binary Pattern (RCLBP) for Surface Defect Detection
Gyimah, N. K., Girma, A., Mahmoud, M. N., Nateghi, S., Homaifar, A., & Opoku, D. (2021) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc52423.2021.9659140 · Cited by 26 · see citing works
“In [10], LEDNet based on Convolutional Neural Network (CNN) was proposed to detect and classify defects on LED chips.”
MethodLu, B., & Huang, B. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02105-4 · Cited by 25 · see citing works
“Lin et al. (2019) adapt a CNN for LED chips defect detection and use class activation mapping to find the approximate location of defects based on the trained CNN. Xu et al. (2021) design the de-deformation defect detection network (D4Net) to detect defects in non-grid fabrics with large patterns.”
AI on edge device for laser chip defect detection
Hou, D., Liu, T., Pan, Y.-T., et al. (2019) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc.2019.8666503 · Cited by 25 · see citing works
TL-SDD: A Transfer Learning-Based Method for Surface Defect Detection with Few Samples
Cheng, J., Guo, B., Liu, J., Liu, S., Wu, G., Sun, Y., & Yu, Z. (2021) · International Conference on Big Data Computing and Communications (BigCom) · doi:10.1109/bigcom53800.2021.00023 · Cited by 24 · see citing works
“Lin [12] et al. used CAM to obtain the heatmap, and used the Otsu binarization method to segment the heatmap to realize the location of scratches or line defects in the LED lamp image.”
MethodRoad Crack Detection Using Deep Neural Network Based on Attention Mechanism and Residual Structure
Jing, P., Yu, H., Hua, Z., Xie, S., & Song, C. (2022) · IEEE Access · doi:10.1109/access.2022.3233072 · Cited by 24 · see citing works
“Lin H et al. [11] proposed LEDNet neural network for defect detection of LED chips, and achieved high detection results.”
BackgroundRožanec, J. M., Bizjak, L., Trajkova, E., Zajec, P., Keizer, J., Fortuna, B., & Mladenić, D. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02098-0 · Cited by 24 · see citing works
“Lin et al. (2019) describes how defect inspection on LED chips was automated using deep Convolutional Neural Networks (CNN).”
Steel Surface Defect Detection Method Based on Improved YOLOv9 Network
Zou, J., & Wang, H. (2024) · IEEE Access · doi:10.1109/access.2024.3453931 · Cited by 25 · see citing works
“Lin et al. [9] utilized convolutional neural networks for LED chip defect detection.”
BackgroundTang, W., Vian, C., Tang, Z., & Yang, B. (2021) · Machine Vision and Applications · doi:10.1007/s00138-021-01226-1 · Cited by 23 · see citing works
A gated multi-hierarchical feature fusion network for recognizing steel plate surface defects
Tao, H., Lu, M., Hu, Z., & An, J. (2023) · Multimedia Systems · doi:10.1007/s00530-023-01066-1 · Cited by 23 · see citing works
“[3] propose a class activation mapping technique in a CNN for light-emitting diode chip defect inspection to localize defects without using region-level human annotations.”
Efficient Micro-LED defect detection based on microscopic vision and deep learning
Chen, M., Han, S., & Li, C. (2024) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2024.108116 · Cited by 23 · see citing works
Jiang, X., Zheng, J., Chen, Z., et al. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3454418 · Cited by 22 · see citing works
“Although the overall performance of the model is quite impressive, rare faults can lead to significant damage and risk [6], [7], [8].”
Molitor, D. A., Kubik, C., Becker, M., Hetfleisch, R. H., Lyu, F., & Groche, P. (2021) · Journal of Materials Processing Technology · doi:10.1016/j.jmatprotec.2021.117484 · Cited by 22 · see citing works
Synthetic Data for Defect Segmentation on Complex Metal Surfaces
Fulir, J., Bosnar, L., Hagen, H., & Gospodnetić, P. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00465 · Cited by 22 · see citing works
“CAMs tend to produce very localized predictions, which are useful for defect segmentation as defects are often localized, such as defects in LED chips [27].”
BackgroundLocal–global lightweight ViT model for mini/micro-LED-chip defect recognition
Wei, L., Cai, J., Wen, K., & Zhang, C. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106247 · Cited by 21 · see citing works
Mei, S., Cheng, J., He, X., Hu, H., & Wen, G. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3131908 · Cited by 19 · see citing works
Meng, Y., Xu, H., Ma, Z., Zhou, J., & Hui, D. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03671-5 · Cited by 19 · see citing works
“used AlexNet architecture and classified activation method to classify LED defect images [35].”
MethodReal-time defect detection network for polarizer based on deep learning
Liu, R., Sun, Z., Wang, A., et al. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01536-7 · Cited by 18 · see citing works
LED Chip Defect Detection Method Based on a Hybrid Algorithm
Zheng, P., Lou, J., Wan, X., Luo, Q., Li, Y., Xie, L., & Zhu, Z. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/4096164 · Cited by 17 · see citing works
Robust Inspection of Micro-LED Chip Defects Using Unsupervised Anomaly Detection
Park, S., & Ko, J. H. (2021) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc52510.2021.9620801 · Cited by 17 · see citing works
Integrated Circuit Gold Wire Bonding Measurement Via 3-D Point Cloud Deep Learning
Xie, Q., Long, K., Lu, D., Li, D., Zhang, Y., & Wang, J. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3120473 · Cited by 16 · see citing works
Surface defects inspection of cylindrical metal workpieces based on weakly supervised learning
Ye, M., Zhang, W., Cui, G., et al. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-08399-z · Cited by 16 · see citing works
“Lin et al. [26] used CAM method to visually predict the depth CNN and thus locate the LED defect location.”
MethodSelf-citationA Deep Learning-Based Framework for Visual Inspection of Plastic Bottles
Kazmi, M., Hafeez, B., Aftab, F., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3329565 · Cited by 15 · see citing works
“There are various industrial applications for such visual inspection systems including defect detection on steel [9], glass bottles [10], welding [11], [12], fabric [13], wood [14], roller [15], railways [16], LEDs [17] and plastic products [18].”
BackgroundTatzel, L., Tamimi, O. A., Haueise, T., & León, F. P. (2021) · Optics & Laser Technology · doi:10.1016/j.optlastec.2021.107028 · Cited by 15 · see citing works
Bártová, B., Bína, V., & Váchová, L. (2022) · Production · doi:10.1590/0103-6513.20210097 · Cited by 17 · see citing works
- Authors
- Lin, H., Li, B., Wang, X., Shu, Y., & Niu, S.
- Affiliations
- State Key Laboratory of Digital Manufacturing Equipment and Technology (CN)
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Li, Bin
- Venue
- Journal of Intelligent Manufacturing
- Published
- 2018-03-29
- Volume
- 30
- Issue
- 6
- Pages
- 2525-2534
- ISSN
- 0956-5515 (ISSN-L)
- 1572-8145
- OpenAlex
- W2794550100
- Semantic Scholar
- 358b9707c2d45c06e23a264d21390c2ed5a1d7ee
- Cited by
- 241
- References
- 34
- Citations by year
- 2026: 13 · 2025: 25 · 2024: 34 · 2023: 36 · 2022: 36 · 2021: 40 · 2020: 40 · 2019: 11 · 2018: 6
- FWCI
- 19.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Intelligent Manufacturing (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- The 8th batch of 3551 Optics Valley Talent Schema in East Lake Hi-tech Zone- LED chip automatic detection sorting key technology and sets of equipment development and industrialization
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- defect localization, class activation mapping, scratch marks, automated visual inspection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
