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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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Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
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
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
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
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
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 44,708
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
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
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
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
Cited by
Sorted by citations
Items 101–200 of 238
Wang, Z., Gong, S., Li, D., et al. (2018) · ISA Transactions · doi:10.1016/j.isatra.2018.11.026 · Cited by 15
A multi-subpopulation genetic algorithm-based CNN approach for ceramic tile defects classification
Huynh, N.-T. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02130-3 · Cited by 14
“Although, Convolutional Neural networks are widely used for images processing (Krizhevsky et al., 2012; LeCun et al., 2015; Lin et al., 2019; Sainath et al., 2013), constructing an optimal CNN model for a specific problem requires time and domain knowledge.”
Usformer: A small network for left atrium segmentation of 3D LGE MRI
Lin, H. Z., López-Tapia, S., Schiffers, F., et al. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e28539 · Cited by 14
“Convolutional neural networks (CNNs) have demonstrated a high level of effectiveness across various applications, like pixel-wise detection of defects with complex and varied shapes [17,24,27,38].”
BackgroundSelf-citationNahar, L., Islam, M. S., Awrangjeb, M., & Verhoeve, R. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104187 · Cited by 19
IC solder joint inspection via generator-adversarial-network based template
Li, J., Cai, N., Mo, Z., et al. (2021) · Machine Vision and Applications · doi:10.1007/s00138-021-01218-1 · Cited by 14
Lin, Y., Pan, S., Yu, J., Hong, Y., Wang, F., Zheng, L., Tang, J., & Chen, S. (2025) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2025.109082 · Cited by 13
Moon, I. Y., Lee, H. W., Kim, S., Oh, Y., Jung, J., & Kang, S.-H. (2021) · Materials · doi:10.3390/ma14092095 · Cited by 13
“[27] also showed that by applying the CNN together with the CAM for the surface defect inspection, the CNN’s classification criterion can be indirectly understood.”
MethodChip Appearance Defect Recognition Based on Convolutional Neural Network
Wang, J., Zhou, X., & Wu, J. (2021) · Sensors · doi:10.3390/s21217076 · Cited by 13
“Method Lin’s [17] Redmon’s [18] Chen’s [19] Fu’s [20] Our Proposed”
Intelligent Micron Optical Character Recognition of DFB Chip Using Deep Convolutional Neural Network
Wang, X., Li, Y., Liu, J., et al. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3154831 · Cited by 13
Self-citationPermute-MAML: exploring industrial surface defect detection algorithms for few-shot learning
Pang, S., Zhao, W., Wang, S., Zhang, L., & Wang, S. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01219-9 · Cited by 15
Visual Defect Inspection for Deep-Aperture Components With Coarse-to-Fine Contour Extraction
Gong, X., Su, H., Xu, D., Zhang, J., Zhang, L., & Zhang, Z. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2928347 · Cited by 13
“Lin et al. [39] built a CNN for light-emitting diode chip detection.”
BackgroundWeakly-Supervised Defect Segmentation on Periodic Textures Using CycleGAN
Kim, M.-S., Jo, H., Ra, M., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3024554 · Cited by 12
“A network called LEDNet, which is based on CAM for classifying and localizing defects in LED chip images, was presented [39].”
MethodWu, Z., Cai, N., Chen, K., Xia, H., Zhou, S., & Wang, H. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02146-9 · Cited by 13
“Among these studies, some related works inspect the surfaces of the chips (Du et al., 2020; Lin et al., 2019), which are similar to IC package inspection.”
Cheng, Z., Wang, H., & Liu, G. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01599-6 · Cited by 12
“…neural network (DCNN) and its superiority in solving image recognition and classification problems during engineering and manufacturing processes (Lin et al. 2019; Badmos et al. 2019; Tabernik et al. 2020; Kwon et al. 2020), A DCNN aided optimization (DCNNAO) method is proposed to improve the…”
MethodAutomatic compact camera module solder joint inspection method based on machine vision
Peng, Y., Yan, Y., Chen, G., & Feng, B. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac769a · Cited by 12
Micro LED defect detection with self-attention mechanism-based neural network
Zhong, Z., Li, C., Chen, M., Wu, H. Y., & Kiyoshi, T. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104474 · Cited by 11
Zhou, D., Gong, S., Wang, Z., et al. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01615-9 · Cited by 11
Defect Detection on LED Chips Based on Position Pre-Estimation and Feature Enhancement
Xu, L., Hu, X., He, T., Hu, K., & Zhang, J. (2022) · Applied Sciences · doi:10.3390/app12031265 · Cited by 11
Defect-Net: a new CNN model for steel surface defect classification
Abbes, W., Elleuch, J. F., & Sellami, D. (2024) · International Symposium on Signal, Image, Video and Communications (ISIVC) · doi:10.1109/isivc61350.2024.10577945 · Cited by 12
Defect Contour Detection of Complex Structural Chips
Lin, B., Wang, J., Yang, X., Tang, Z., Li, X., Duan, C., & Zhang, X. (2021) · Mathematical Problems in Engineering · doi:10.1155/2021/5518675 · Cited by 11
Adaptive vision inspection for multi-type electronic products based on prior knowledge
Zhao, D., Xue, D., Wang, X., & Du, F. (2021) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2021.100283 · Cited by 12
Self-citationA weighted multi‐source domain adaptation approach for surface defect detection
Hu, B., & Wang, J. (2022) · IET Image Processing · doi:10.1049/ipr2.12484 · Cited by 11
Wang, J., Yi, G., Zhang, S., & Wang, Y. (2020) · Applied Sciences · doi:10.3390/app11010283 · Cited by 10
Kamoona, A. M., Gostar, A. K., Bab‐Hadiashar, A., & Hoseinnezhad, R. (2021) · IEEE Access · doi:10.1109/access.2021.3130261 · Cited by 11
Brighteye: Glaucoma Screening with Color Fundus Photographs Based on Vision Transformer
Lin, H. Z., Apostolidis, C., & Katsaggelos, A. K. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635883 · Cited by 9
“Convolutional neural networks (CNNs) have presented a promising performance in multiple applications in manufacturing [2, 12] as well as healthcare [1, 3].”
BackgroundSelf-citationImproving Accuracy of IC Surface Defects Detection via Enhanced-CycleGAN Data Augmentation
Alam, L., & Kehtarnavaz, N. (2025) · IEEE Access · doi:10.1109/access.2025.3541614 · Cited by 11
Siamese network with change awareness for surface defect segmentation in complex backgrounds
Liu, B., Luo, S., Zhan, H., Zhou, Y., Huang, Z., & Chen, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-94733-4 · Cited by 12
“A class activation map 11 is widely used to indicate the potential anomalous regions within an image with only image-level hints 3,12 .”
DFB Laser Chip Defect Detection Based on Successive Subspace Learning
Hou, D., Liu, T., Zhang, X., et al. (2020) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc47524.2020.9031246 · Cited by 9
“[2-6] To reduce the complexity of deep learning, a sequence of research work has been conducted by Professor Kuo and his students at the University of Southern California in the last five years.”
MethodWeng, W.-H., Tsai, C., Hung, C.-Y., & Kuo, C. J. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-07774-0 · Cited by 8
An approach for classifying ceramic tile defects based on a two-dimensional Genetic CNN algorithm
Huynh, N.-T. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09012-y · Cited by 8
Chassis Assembly Detection and Identification Based on Deep Learning Component Instance Segmentation
Liu, G., He, B., Liu, S., & Huang, J. F. (2019) · Symmetry · doi:10.3390/sym11081001 · Cited by 8
Guo, S., Li, D., Zhao, J., Jia, H., Luo, B., Bao, T., & Lv, Y. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02079-y · Cited by 7
“Hui et al proposed an LEDNet based CNN and achieved high performance on inspection line blemishes and scratch marks of LED chip [24].”
FruitVision: Dual-Attention Embedded AI System for Precise Apple Counting Using Edge Computing
Thakur, D., & Kumar, V. (2024) · IEEE Transactions on AgriFood Electronics · doi:10.1109/tafe.2024.3416221 · Cited by 7
Li, F., Cai, N., Deng, X., Li, J., Lin, J., & Wang, H. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01730-7 · Cited by 7
Balcıoğlu, Y. S., Sezen, B., Çerasi, C. Ç., & Huang, S. H. (2023) · Electronics · doi:10.3390/electronics12040825 · Cited by 7
Wu, H., Liu, Y., & Xu, Y. (2023) · Electronics · doi:10.3390/electronics12245037 · Cited by 7
RMDTs: Process-oriented function-triggered robotic milling digital twin system for service-expansion
Zhao, S., Peng, F., Shan, Y., Su, J., Tang, X., & Yan, R. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.07.015 · Cited by 6
Multi-modal background-aware for defect semantic segmentation with limited data
Shan, D., Zhang, Y., & Liu, S. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02373-8 · Cited by 6
“…2015; Ronneberger et al., 2015; Zhao et al., 2017), the main idea for defect segmentation is to fuse multi-scale features (Cao et al., 2020; Dong et al., 2019, 2022; Lin et al., 2019; Tabernik et al., 2020; Xie et al., 2019) or introduce the attention mechanism (Dong et al., 2019; Ni et al., 2021).”
Bi-deformation-UNet: recombination of differential channels for printed surface defect detection
Chen, Z., Huang, G., Wang, Y., et al. (2022) · The Visual Computer · doi:10.1007/s00371-022-02554-7 · Cited by 6
Segmentation-based Decision Networks for Steel Surface Defect Detection
Bi, Z., Bi, Q. W. Z., Wu, M. S. Q., & Shan, W. Z. M. (2022) · 網際網路技術學刊 · doi:10.53106/160792642022112306022 · Cited by 6
Defect Segmentation in Surfaces using Deep Learning
Mittal, S., Chopra, C., Trivedi, A., & Chanak, P. (2019) · International Conference on Issues and Challenges in Intelligent Computing Techniques · doi:10.1109/icict46931.2019.8977696 · Cited by 6
“Lin et al (2018) (17) defined the LEDNet (Light Emitting Diode Network) architecture in their paper.”
BackgroundBinary Neural Network for Automated Visual Surface Defect Detection
Liu, W., Zhang, J., Su, Z., Zhou, Z., & Liu, L. (2021) · Sensors · doi:10.3390/s21206868 · Cited by 6
“In rapidly developing modern industry, the detection procedure can be exceedingly repetitive and exhausting; thus, computer vision methods have dominated in the automatic quality assessment of diverse industrial products, covering bolts [15], fasteners [16], LED chips [17], etc.”
BackgroundUncertainty-Aware Adaptive Multiscale U-Net for Low-Contrast Cardiac Image Segmentation
Sagar, A. S. M. S., Islam, M. Z., Tanveer, J., et al. (2025) · Applied Sciences · doi:10.3390/app15042222 · Cited by 5
“These limitations have accelerated the adoption of deep learning-based approaches, particularly Convolutional Neural Networks (CNNs), which have demonstrated remarkable success in cardiac imaging tasks [10–13].”
Wang, X., Liu, L., Ye, Y., et al. (2022) · Optics Express · doi:10.1364/oe.469170 · Cited by 5
Self-citationHierarchical active learning for defect localization in 3D systems
Xie, J., & Yao, B. (2023) · IISE Transactions on Healthcare Systems Engineering · doi:10.1080/24725579.2023.2233992 · Cited by 5
“Lin et al. (2019) build a CNN to localize defect regions of light-emitting diode chips without using region-level human annotations.”
BackgroundMultiple Detection Model Fusion Framework for Printed Circuit Board Defect Detection
Wu, X., Zhang, Q., Wang, J., Yao, J., & Guo, Y. (2022) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-022-2471-0 · Cited by 5
“Lin et al. [30] applied convolutional neural network (CNN) to light-emit-ting diode (LED) chip defect detection and classification tasks and they achieved good performance.”
BackgroundDey, B. K., & Seok, H. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.100936 · Cited by 4
Chen, J., Yin, H., Qin, Y., & Jiang, W. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3413982 · Cited by 4
“Lin et al. [16] applied deep CNNs in the field of LED defect recognition for the first time and achieved the best results at the time, but the few-shot problem regarding defects has not been addressed in this work.”
BackgroundDeep learning-based detection of wine bottle capsules for contamination prevention
Caballero-Ramírez, D., Tortorella, G. L., García-Alcaráz, J. L., et al. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.106388 · Cited by 4
Wang, Z., Gong, S., Li, D., et al. (2020) · ISA Transactions · doi:10.1016/j.isatra.2020.03.030 · Cited by 4
Domain Randomization Using Deep Neural Networks for Estimating Positions of Bolts
Ameperosa, E., & Bhounsule, P. A. (2020) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4047074 · Cited by 4
“Some of these applications include crack detection in nuclear power plant components [23], LED chip defect inspection [24], monitoring faults on rotating machinery [25], detection of fabric defects [26], solder joints defects [27], defects in composite laminate beams [28].”
BackgroundImage Processing with Deep Learning: Surface Defect Detection of Metal Gears through Deep Learning
Balcıoğlu, Y. S., Sezen, B., Gök, M. Ş., & Tunca, S. (2022) · Materials Evaluation · doi:10.32548/2022.me-04230 · Cited by 5
An Automatic Foreign Matter Detection and Sorting System for PVC Powder
Chen, S., Jang, J., Chang, Y.-R., Kang, C.-H., Chen, H.-Y., Liu, K. F.-R., Lee, F.-L., Hsueh, Y.-S., & Youh, M.-J. (2022) · Applied Sciences · doi:10.3390/app12126276 · Cited by 4
“Lin et al. [22] employed CNN to identify three types of defect on LED chips and utilized class activation mapping (CAM) to locate the defects.”
Research on YOLOX-based tire defect detection method
Qinghua, Q., Xu, X., Liu, Z., et al. (2022) · CAA International Conference on Vehicular Control and Intelligence (CVCI) · doi:10.1109/cvci56766.2022.9964564 · Cited by 4
“Deep learning-based defect detection is also used in defect detection on mechanical surfaces([11]), LED chips([12]), and railway catenary([13]).”
MethodAn industrial defect detection algorithm based on CPU-GPU parallel call
Zhu, L., Lin, H.-W., Liu, Y., Chen, C., & Xia, Y.-F. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15613-5 · Cited by 5
“Lin H. et al. [12] proposed a LED-Net network for detecting LED chip image defects based on the AlexNet framework [13.”
MethodSelf-citationA Novel Detection Method for Valve Damage of Nuclear Power Using Attention-Based U-Net (ABUNet)
Cao, S., Shen, S., Fan, S., Wang, X., Zhu, Z., Shi, Y., & Lou, J. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3303012 · Cited by 4
“recognition is not always clear [11], [12], [13], and feature determination is too subjective such that human experience often plays a decisive role.”
BackgroundSelf-citationDeepCornerNet: A Deep Learning Approach for Automated Corner Grading in Trading Cards
Nahar, L., Islam, M. S., Awrangjeb, M., et al. (2023) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta60407.2023.00013 · Cited by 4
“Lin et al. [13] implemented a CNN-based model, LEDNet, for defect classification on LED chips, achieving 95% accuracy.”
MethodDeepSC-Edge: Scientific Corrosion Segmentation with Edge-Guided and Class-Balanced Losses
Yin, B., Josselyn, N., Considine, T. A., et al. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00251 · Cited by 4
“For corrosion science, machine learning solutions have been applied to automate engineering tasks such as defect detection [14]–[17] and corroded pipe detection [18].”
MethodShim, H., & Kim, S. K. (2024) · Micromachines · doi:10.3390/mi15040457 · Cited by 4
“More recently, Lin et al. introduced a method for detecting defective LEDs using convolutional neural networks (CNNs) from images of operational LEDs [10].”
MethodEffective image models for inspecting profile flaws of car mirrors with applications
Chiu, Y.-S. P., Lin, Y.-K., & Lin, H.-D. (2020) · Istrazivanja i projektovanja za privredu · doi:10.5937/jaes18-22825 · Cited by 4
“[4] applied the convolutional neural network with deep learning to conduct automatic inspection of light-emitting diode chips.”
MethodSelf-citationYang, Q., Xing, Y., & Du, J. (2020) · International Conference on Innovation in Artificial Intelligence (ICIAI) · doi:10.1145/3390557.3394322 · Cited by 4
“Various defect detection methods are widely used in various products, such as textile defect detection [1], tire defect detection [2], surface defect detection of steel strip [3], LED chip defect detection [4].”
BackgroundMethodDefect Detection for Mechanical Design Products with Faster R-CNN Network
Liu, P., Su, S., Gao, X., Zheng, H., & Ma, Z. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/3209721 · Cited by 4
Selvi, M. K., Devi, R. M., Soundararajan, E. K., Anandaraj, S., Priya, G. S., Shanavas, S., & Kumar, P. M. (2025) · Buildings · doi:10.3390/buildings16010154 · Cited by 3
Coupling of unsupervised and supervised deep learning-based approaches for surface anomaly detection
Rački, D., Tomaževič, D., & Skočaj, D. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.3.031207 · Cited by 3
Convolutional neural network-based simultaneous display-communication system
Fu, K., Fu, J., Wu, W., Ye, Z., Wang, B., Yan, J., Shi, F., Liu, P., & Wang, Y. (2025) · APL Photonics · doi:10.1063/5.0239393 · Cited by 4
Relational-based transfer learning for automatic optical inspection based on domain discrepancy
Salgado, E. I. V., Yan, H., Hong, Y., Zhu, P., Zhu, S., Liao, C., Wen, Y., Li, X., Qian, X., Wang, X., et al. (2022) · Optoelectronic Imaging and Multimedia Technology · doi:10.1117/12.2644087 · Cited by 3
“Other research in this field pointed out typical techniques to deal with the lack of samples by data augmentation [10], [11], transfer learning-aided models pretrained on benchmark datasets without domain similarities with the target domain [12], [13], specialized network structures to extract…”
BackgroundSelf-citationThe Prior Model-Guided Network for Bearing Surface Defect Detection
Feng, H., Zhuang, J., Chen, X., Song, K., Xiao, J., & Ye, S. (2023) · Electronics · doi:10.3390/electronics12051142 · Cited by 3
An Improved GAN Model Based on Positive Samples for LED Die Defect Detection
Shao, G., Chen, H., & Gao, F. (2022) · Asian Conference on Artificial Intelligence Technology (ACAIT) · doi:10.1109/acait56212.2022.10137955 · Cited by 3
“Chen et al. put forward a YOLOv3-dense model to solve the problems of surface defects, missing components, and inverse polarity in SMD LED chips, it performs better than CNN and YOLOv3 [10].”
InfluentialUsformer: A Light Neural Network for Left Atrium Segmentation of 3D LGE MRI
Lin, H. Z., López-Tapia, S., Schiffers, F., et al. (2023) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco58844.2023.10289839 · Cited by 3
“Convolutional neural networks (CNNs) have presented a promising performance in multiple applications [2], [3].”
BackgroundSelf-citationWang, X., Liu, L., Liu, J., Zhou, Y., Du, X., Zhang, J., Du, H., Ye, Y., & Liu, Y. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3351236 · Cited by 3
“Therefore, this kind of contamination or defect caused by its own physical process is unavoidable, and this is generally considered to be factors from physical mechanisms [20].”
BackgroundMethodInfluentialSelf-citationHuynh, N.-T., Ho, D.-D., & Nguyen, H.-N. (2023) · Sustainability · doi:10.3390/su15065455 · Cited by 4
Apparatus and Method of Defect Detection for Resin Films
Sheu, R., Teng, Y.-H., Tseng, C.-H., & Chen, L.-C. (2020) · Applied Sciences · doi:10.3390/app10041206 · Cited by 3
“These machine vision-based methods are used in the inspection of many industrial products, such as metal [1], fabric [2], steel [3], car surfaces [4], and light-emitting diode chips [5].”
MethodTransformer-based Encoder-Decoder Model for Surface Defect Detection
Lu, X., & Fan, W. (2022) · International Conference on Innovation in Artificial Intelligence (ICIAI) · doi:10.1145/3529466.3529471 · Cited by 2
“[13] designed a detection LED Net architecture for LED chip defects, which follows the Alex Net [14] architecture, but removes the fully connected layer and replaces it with CAMs, an architecture that allows them to learn information about each image label and use CAMs to locate”
MethodMOSS: AI Platform for Discovery of Corrosion-Resistant Materials
Yin, B., Josselyn, N., Zhang, Z., et al. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614748 · Cited by 2
Computer vision‐based automated defect detection in ceramic bricks
Kataev, M. Y., & Bulysheva, L. (2024) · Systems Research and Behavioral Science · doi:10.1002/sres.3040 · Cited by 3
Design of LQFP chip pin defect detection system based on machine vision
Ou, X., Chen, W., & Zhang, M. (2021) · International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) · doi:10.1109/mlise54096.2021.00021 · Cited by 2
Camera module Lens blemish detection based on neural network interpretability
Yang, M., Wu, J. Y., & Niu, X. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11716-z · Cited by 2
“Several metrics, such as inaccuracy, error recognition rate, detection precision, Average precision, recall, mean average recall, Intersection-over-Union, Loose-IoU and area under the recall curve are used to evaluate object detection [8, 25].”
MethodCross-Domain Defect Detection Network
Zhou, Z., Lan, C., & Gao, Z. (2022) · Asia Conference on Algorithms, Computing and Machine Learning (CACML) · doi:10.1109/cacml55074.2022.00053 · Cited by 2
“Lin et al. [5] constructed a LEDNet to detect the defects on LED chips.”
BackgroundTransferring Indoor Corrosion Image Assessment Models to Outdoor Images via Domain Adaptation
Josselyn, N., Yin, B., Considine, T. A., et al. (2022) · IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla55696.2022.00219 · Cited by 2
“In the past several years, some deep learning models with respect to the detection of material defects have been explored, including LEDNet [7], Faster Region-based CNN [8] [9], fully connected networks [10], and Texture CNN – with the latter on 150 raw corroded pipe images [11].”
MethodToward Fault Detection in Industrial Welding Processes with Deep Learning and Data AugmentationPreprint
Antony, J., Schlather, F., Safronov, G. S., Schmitz, M., & Laerhoven, K. V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.10160 · Cited by 2
“A CNN based defect classification network called LEDNet, used in the production control of LED lights, has been developed by the authors of [9] in 2018.”
MethodSACNN: Spatial Adversarial Convolutional Neural Network for Textile Defect Detection
Hou, W. S., Tao, X., Ma, W., & Xu, D. (2020) · Fibres and Textiles in Eastern Europe · doi:10.5604/01.3001.0014.3808 · Cited by 1
“proposed a class activation mapping (CAM) technique to localise defect regions without using region-level human annotation [9].”
MethodClassification testa color level of cashew kernel by feed forward back propagation neural network
Phromfaiy, A., Wangsoh, N., & Surin, P. (2024) · AIP conference proceedings · doi:10.1063/5.0214331 · Cited by 1
MenUnet: An End-to-End 3D Neural Network for Meningioma Segmentation from Multiparametric MRI
Lin, H. Z., Cheng, X., & Chen, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-76163-8_26 · Cited by 1
Self-citationVigneshwaran, R., E., S., & Shoba, R. (2025) · doi:10.1109/icdsaai65575.2025.11011763 · Cited by 1
Nondestructive Single-Contact Electroluminescence Inspection of LED With High Accuracy
Huang, J., Zhi, T., Tao, T., et al. (2025) · IEEE Electron Device Letters · doi:10.1109/led.2025.3599212 · Cited by 1
“The automated visual inspection can only detect surface defects [25].”
Superpixel-based regional anomaly detection for LED chips using local statistical features
Zhang, H., Liu, T., Tang, K., et al. (2025) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2025.109281 · Cited by 1
Zhang, F., Pan, B., Wang, Y., Zhou, C., Zhao, Y., & Duan, J. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae527b · Cited by 1
“Lin et al [11] proposed a convolutional neural network for detecting defects on the surface of LED chips, which uses an attention pyramid network in the decoder while combining the convolutional neural network with the class activation mapping technique.”
Defect Detection and Recognition of Mobile Phone Membrane Based on Convolutional Neural Network
Li, C., Zhang, E., & Liu, L. (2021) · EAI International Conference on Green Energy and Networking (GreeNets) · doi:10.4108/eai.6-6-2021.2307711 · Cited by 1
“Established a convolutional neural network (CNN) for fault checking of LED chips [23].”
BackgroundAutomated Shell Abrasion Detection for Cell Phone Based on GraphFPN With Learnable Sparse Priors
Wu, H., Guo, X., Xiao, F., Yan, W., & Wen, Z. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3150591 · Cited by 1
Weakly supervised surface defect classification network based on Gaussian model
Song, K., Feng, H., Peng, C., Zhao, M. L., Tian, X., Liao, X., & Xiao, J. (2022) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp54964.2022.9778408 · Cited by 1
“And meanwhile, various unsupervised [3,4,5] and weakly supervised [6,7,8] 978-1-6654-7857-1/22/$31.00©2022 IEEE to reduce the need for large amounts of annotated data.”
BackgroundInfluentialChip detection algorithm based on lightweight E-YOLOv5 convolutional neural network
Zhai, X., Huang, M., & Wei, H. (2023) · Engineering Research Express · doi:10.1088/2631-8695/acc1c2 · Cited by 1
A Self-Training-Based System for Die Defect Classification
Wu, P.-H., Lin, S.-Z., Chang, Y.-T., et al. (2024) · Mathematics · doi:10.3390/math12152415 · Cited by 1
An Automated Template Making Method for Visual Measurement of Microwave Probes
Wu, T., Chen, L., Zuo, N., et al. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3507039 · Cited by 1
“Currently, image recognition, segmentation, and measurement are widely used in the industry [12], [13], [14], [15], [16].”
Lee, J., & Jin, J. (2025) · International Journal of Precision Engineering and Manufacturing-Smart Technology · doi:10.57062/ijpem-st.2024.00164 · Cited by 3
“In the field of measurement and inspection, pattern recognition and classification of images acquired through cameras are representative applications [11-16].”
DVDSNet: A Novel VCSEL Dark-line Defect Segmentation Model Based on Dual Encoder
Li, D., Hu, Y., Zhao, J., et al. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-024-09904-8 · Cited by 1
Ndukwe, I. K., & Boby, R. A. (2025) · Lecture notes in mechanical engineering · doi:10.1007/978-981-97-6176-0_17 · Cited by 1
Conductive Particles Detection in the TFT-LCD Manufacturing Process with U-ResNet
Chen, K., & Liu, E. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03341-5_14 · Cited by 1
PM-AE: Pyramid Memory Autoencoder for Unsupervised Textured Surface Defect Detection
Yao, H., Li, D., Zhu, Y., & Yu, W. (2020) · International Conference on Mechanical, Control and Computer Engineering (ICMCCE) · doi:10.1109/icmcce51767.2020.00290 · Cited by 1
“Supervised approaches [7][8][9] achieve state-of-the-art performance when given a large number of labeled data.”
BackgroundChiu, Y.-S. P., Lin, H.-D., & Cheng, H.-H. (2021) · Journal of Applied Research and Technology · doi:10.22201/icat.24486736e.2021.19.4.958 · Cited by 1
Self-citationInspecting Decorative Ceramic Defects by Fusing Convolutional Neural Network and Image Recognition
Jin, K., & Wang, C. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/3983919 · Cited by 1
“Defect inspection is also an important link in the production process of decorative ceramics, and it is also essential to control the quality [4].”
BackgroundRetracted
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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