CHAUDHARY V, DAVE I R, UPLA K P. Automatic visual inspection of printed circuit board for defect detection and classification [C]//2017 International Conference on Wireless Communications, Signal Processing and Networking. Chennai: IEEE, 2017: 732–737.
ZHU JH, WU A, LIU X P. Printed circuit board defect visual detection based on wavelet denoising [J]. IOP Conference Series: Materials Science and Engineering, 2018, 392: 062055.
KUO C F J, FANG T Y, LEE C L, et al. Automated optical inspection system for surface mount device light emitting diodes [J]. Journal of Intelligent Manufacturing, 2019, 30(2): 641–655.
KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks [J]. Communications of the ACM, 2017, 60(6): 84–90.
WU X, ZHONG M Y, GUO Y K, et al. The assessment of small bowel motility with attentive deformable neural network [J]. Information Sciences, 2020, 508: 22–32.
LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot multibox detector [M]//Computer vision -ECCV 2016. Cham: Springer, 2016: 21–37.
REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection [C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV: IEEE, 2016: 779–788.
LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI: IEEE, 2017: 936–944.
JIANG H Z, LEARNED-MILLER E. Face detection with the faster R-CNN [C]//2017 12th IEEE International Conference on Automatic Face & Gesture Recognition. Washington, DC, USA: IEEE, 2017: 650–657.
WANG Y, LUO X B, DING L, et al. Detection based visual tracking with convolutional neural network [J]. Knowledge-Based Systems, 2019, 175: 62–71.
WEI H, YANG C Z, YU Q. Efficient graph-based search for object detection [J]. Information Sciences, 2017, 385/386: 395–414.
BRIA A, MARROCCO C, MOLINARA M, et al. An effective learning strategy for cascaded object detection [J]. Information Sciences, 2016, 340/341: 17–26.
OLSON R S, MOORE J H. TPOT: A tree-based pipeline optimization tool for automating machine learning [M]//Automated machine learning. Cham: Springer, 2019: 151–160.
HOSANG J, BENENSON R, SCHIELE B. Learning non-maximum suppression [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI: IEEE, 2017: 6469–6477.
DING R W, DAI L H, LI G P, et al. TDD-net: a tiny defect detection network for printed circuit boards [J]. CAAI Transactions on Intelligence Technology, 2019, 4(2): 110–116.
TANG S L, HE F, HUANG X L, et al. Online PCB defect detector on a new PCB defect dataset [DB/OL]. (2019-02-17). https://arxiv.org/abs/1902.06197.
DALAL N, TRIGGS B. Histograms of oriented gradients for human detection [C]//2005 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition. San Diego, CA: IEEE, 2005: 886–893.
LOWE D G. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision, 2004, 60(2): 91–110.
BAY H, TUYTELAARS T, VAN GOOL L. SURF: speeded up robust features [M]//Computer vision — ECCV 2006. Berlin, Heidelberg: Springer, 2006: 404–417.
LU Z S, HE Q Q, XIANG X G, et al. Defect detection of PCB based on Bayes feature fusion [J]. The Journal of Engineering, 2018, 2018(16): 1741–1745.
BENEDEK C. Detection of soldering defects in printed circuit boards with hierarchical marked point processes [J]. Pattern Recognition Letters, 2011, 32(13): 1535–1543.
GAIDHANE V H, HOTE Y V, SINGH V. An efficient similarity measure approach for PCB surface defect detection [J]. Pattern Analysis and Applications, 2018, 21(1): 277–289.
ZHANG C, SHI W, LI X F, et al. Improved bare PCB defect detection approach based on deep feature learning [J]. The Journal of Engineering, 2018, 2018(16): 1415–1420.
DAI W T, MUJEEB A, ERDT M, et al. Soldering defect detection in automatic optical inspection [J]. Advanced Engineering Informatics, 2020, 43: 101004.
BENJDIRA B, KHURSHEED T, KOUBAA A, et al. Car detection using unmanned aerial vehicles: Comparison between faster R-CNN and YOLOv3 [C]//2019 1st International Conference on Unmanned Vehicle Systems-Oman. Muscat: IEEE, 2019: 1–6.
LEI H W, WANG B, WU H H, et al. Defect detection for polymeric polarizer based on faster R-CNN [J]. Journal of Information Hiding and Multimedia Signal Processing, 2018, 9(6): 1414–1420.
LI Y T, HUANG H S, XIE Q S, et al. Research on a surface defect detection algorithm based on MobileNet-SSD [J]. Applied Sciences, 2018, 8(9): 1678.
LI J Y, SU Z F, GENG J H, et al. Real-time detection of steel strip surface defects based on improved YOLO detection network [J]. IFAC-PapersOnLine, 2018, 51(21): 76–81.
HOU W, WEI Y, GUO J, et al. Automatic detection of welding defects using deep neural network [J]. Journal of Physics: Conference Serie, 2017, 933: 012006.
LIN H, LI B, WANG X G, et al. Automated defect inspection of LED chip using deep convolutional neural network [J]. Journal of Intelligent Manufacturing, 2019, 30(6): 2525–2534.
LIN J H, YAO Y, MA L, et al. Detection of a casting defect tracked by deep convolution neural network [J]. The International Journal of Advanced Manufacturing Technology, 2018, 97(1/2/3/4): 573–581.
NASROLLAHI M, BOLOURIAN N, HAMMAD A. Concrete surface defect detection using deep neural network based on lidar scanning [C]//CSCE Annual Conference. Laval: CSCE, 2019: CON032.
MEI S, WANG Y D, WEN G J. Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model [J]. Sensors, 2018, 18(4): 1064.
ADIBHATLA V A, CHIH H C, HSU C C, et al. Defect detection in printed circuit boards using You-only-look-once convolutional neural networks [J]. Electronics, 2020, 9(9): 1547.
ZHANG X, YANG Y H, HAN Z G, et al. Object class detection [J]. ACM Computing Surveys, 2013, 46(1): 1–53.
SENGUPTA A, YE Y T, WANG R, et al. Going deeper in spiking neural networks: VGG and residual architectures [J]. Frontiers in Neuroscience, 2019, 13: 95.
HENDRY, CHEN R C. Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning [J]. Image and Vision Computing, 2019, 87: 47–56.