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Automated defect inspection of LED chip using deep convolutional neural network
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“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]
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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
“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 643
“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
“[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
“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
“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 363
“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
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
Ebayyeh, A. A. R. M. A., & Mousavi, A. (2020) · IEEE Access · doi:10.1109/access.2020.3029127 · Cited by 325
“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 313
“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
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 266
Self-citationKhanam, R., Hussain, M., Hill, R. C., & Allen, P. (2024) · IEEE Access · doi:10.1109/access.2024.3425166 · Cited by 296
“…[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
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
“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
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 110
Davtalab, O., Kazemian, A., Yuan, X., & Khoshnevis, B. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01684-w · Cited by 112
“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
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 101
“…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 100
Sarkar, K., Shiuly, A., & Dhal, K. G. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.134212 · Cited by 100
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
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
“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
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
“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
“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
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 75
On-line part deformation prediction based on deep learning
Zhao, Z., Li, Y., Liu, C., & Gao, J. X. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01465-0 · Cited by 73
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
“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 77
“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
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 75
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 69
“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
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 78
“…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
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 64
Hsu, C.-Y., & Chien, J.-C. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01687-7 · Cited by 63
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
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
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
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
“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
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
“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
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
“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
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
“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
“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
Self-citationMao, Y., Lin, H. Z., Yu, C. X., Frye, R., Beckett, D., Anderson, K., Jacquemetton, L., Carter, F., Gao, Z., Liao, W., et al. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-022-02039-3 · Cited by 50
“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
“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 47
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
“[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
“[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 42
“…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
“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
“…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
“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
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
“defects for individual objects such as LED chips[44], fabrics [58], and metals [6].”
BackgroundChen, A., Li, X., Jing, H., Chen, H., & Li, M. (2023) · Energies · doi:10.3390/en16041619 · Cited by 34
Nahar, L., Awrangjeb, M., & Islam, M. S. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.104067 · Cited by 43
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
Conductive particle detection via deep learning for ACF bonding in TFT-LCD manufacturing
Liu, E., Chen, K., Xiang, Z., & Zhang, J. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01494-9 · Cited by 31
“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
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
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
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
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
Rački, D., Tomaževič, D., & Skočaj, D. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06397-6 · Cited by 30
“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
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 25
“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
“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
“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
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
“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
“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 29
“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
“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
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
“[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
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
“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 24
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
“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
Mei, S., Cheng, J., He, X., Hu, H., & Wen, G. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3131908 · Cited by 20
Meng, Y., Xu, H., Ma, Z., Zhou, J., & Hui, D. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03671-5 · Cited by 19
“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
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
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
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
Surface defects inspection of cylindrical metal workpieces based on weakly supervised learning
Ye, M., Zhang, W., Cui, G., & Wang, X. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-08399-z · Cited by 16
“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
“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 14
Bártová, B., Bína, V., & Váchová, L. (2022) · Production · doi:10.1590/0103-6513.20210097 · Cited by 17
Metadata
- 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
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