[1] Li J, Cheng J. Circle detection method based on the edge-point verification was applied to complex background image[J]. Computer Engineering, 44, 259-263(2018).
[2] Huang W B, Wei P, Zhang M H et al. Hripcb: a challenging dataset for PCB defect detection and classification[J]. The Journal of Engineering, 2020, 303-309(2020).
[3] Su H, Zhang J B, Zhang B H et al. A review of research on the inspection of surface defects based on visual perception[J]. Computer Integrated Manufacturing Systems, 29, 169-191(2023).
[4] Boukouvalas C, Kittler J, Marik R et al. Colour grading of randomly textured ceramic tiles using color histograms[J]. IEEE Transactions on Industrial Electronics, 46, 219-226(1999).
[5] Nand G K, Noopur , Neogi N. Defect detection of steel surface using entropy segmentation[C](2014).
[6] Tsai D M, Heish C. Automated surface inspection of statistical textures[J]. Image and Vision Computing, 21, 307-323(2003).
[7] Choi D C, Jeon Y J, Kim S H et al. Detection of pinholes in steel slabs using Gabor filter combination and morphological features[J]. ISIJ International, 57, 1045-1053(2017).
[8] Xu K, Song M, Yang C L et al. The application of a hidden Markov tree model to on-line detection of surface defects for steel strips[J]. Journal of Mechanical Engineering, 49, 34-40(2013).
[9] Xie X H, Mirmehdi M. TEXEMS: texture exemplars for defect detection on random textured surfaces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 1454-1464(2007).
[10] Shang L D, Yang Q S, Wang J N et al. Detection of rail surface defects based on CNN image recognition and classification[C], 45-51(2018).
[11] Natarajan V, Hung T Y, Vaikundam S et al. Convolutional networks for voting-based anomaly classification in metal surface inspection[C], 986-991(2017).
[12] Liang X. Image-based post-disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimisation[J]. Computer-Aided Civil and Infrastructure Engineering, 34, 415-430(2019).
[13] Guo L Y, Duan H Y, Zhou W W et al. Surface defect detection algorithm of magnetic tile based on Mask R-CNN[J]. Computer Integrated Manufacturing Systems, 28, 1393-1400(2022).
[14] Liu W, Anguelov D, Erhan D et al. SSD: single-shot MultiBox detector[M]. Computer vision-ECCV 2016. Lecture notes in computer science, 9905, 21-37(2016).
[15] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016).
[16] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2015).
[17] Li R S, Zhang Y L, Zhai D H et al. Pin defect detection of the transmission line based on improved SSD[J]. High Voltage Engineering, 47, 3795-3802(2021).
[18] Zhang C B, Chang C C, Jamshidi M. Concrete bridge surface damage detection using a single-stage detector[J]. Computer-Aided Civil and Infrastructure Engineering, 35, 389-409(2020).
[19] Chen R X, Zhan Z, Hu X L et al. Printed circuit board defect detection based on the multi-attentive faster RCNN under noise interference[J]. Chinese Journal of Scientific Instruments, 42, 167-174(2021).
[20] Tao X, Zhang D P, Wang Z H et al. Detection of power line insulator defects using aerial images analysed using convolutional neural networks[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50, 1486-1498(2020).
[22] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).
[23] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection[C], 936-944(2017).
