[1] Li S B, Yang J, Wang Z et al. Review of development and application of defect detection technology[J]. Acta Automatica Sinica, 46, 2319-2336(2020).

[2] Xu P, Geng M, Fang Z et al. Study on high-speed rail defect detection method based on combination of EC and MFL testing[J]. Journal of Mechanical Engineering, 57, 57-65(2021).

[3] Cao Y, Xu C P. Application of an improved threshold segmentation algorithm in lens defect detection[J]. Laser & Optoelectronics Progress, 58, 1610007(2021).

[4] Peng D G, Yin L, Qi E J et al. Power plant pipeline defect detection and segmentation based on OTSU’s and region growing algorithms[J]. Infrared Technology, 43, 502-509(2021).

[5] Orujov F, Maskeliūnas R, Damaševičius R et al. Fuzzy based image edge detection algorithm for blood vessel detection in retinal images[J]. Applied Soft Computing, 94, 106452(2020).

[6] Zhao W D, Chen F, Huang H C et al. A new steel defect detection algorithm based on deep learning[J]. Computational Intelligence and Neuroscience, 2021, 5592878(2021).

[7] Hao B Q, Fan Y G, Song Z H. Deep transfer learning-based pulsed eddy current thermography for crack defect detection[J]. Acta Optica Sinica, 43, 0415002(2023).

[8] Liu L, Ye Y, Guo T L. Detection method for OLED pixel defects based on extended feature pyramid[J]. Acta Optica Sinica, 43, 0212005(2023).

[9] Shi X C, Zhou S K, Tai Y C et al. An improved faster R-CNN for steel surface defect detection[C](2022).

[10] Xiang K, Li S S, Luan M H et al. Aluminum product surface defect detection method based on improved Faster RCNN[J]. Chinese Journal of Scientific Instrument, 42, 191-198(2021).

[11] Ren Q R, Geng J H, Li J Y. Slighter Faster R-CNN for real-time detection of steel strip surface defects[C], 2173-2178(2018).

[12] Zhao Y, Zhang Y C, Sun S H et al. Defect detection method of rebar based on deep learning[J]. Computer Systems & Applications, 30, 87-94(2021).

[13] Weng Y S, Xiao J Q, Xia Y. Strip surface defect detection based on improved mask R-CNN algorithm[J]. Computer Engineering and Applications, 57, 235-242(2021).

[14] Lu Y, Xue L, Wang Y S et al. Surface defect detection of hot rolled strip based on Cascade RCNN[J]. Instrument Technique and Sensor, 101-106, 126(2023).

[15] Sozzi M, Cantalamessa S, Cogato A et al. Automatic bunch detection in white grape varieties using YOLOv3, YOLOv4, and YOLOv5 deep learning algorithms[J]. Agronomy, 12, 319(2022).

[16] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. Computer vision-ECCV 2016, 9905, 21-37(2016).

[17] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 318-327(2020).

[18] Yan X, Yang Y C, Tu N W. Steel surface defect detection based on improved SSD[J]. Modern Manufacturing Engineering, 112-120(2023).

[19] Cheng X, Yu J B. RetinaNet with difference channel attention and adaptively spatial feature fusion for steel surface defect detection[J]. IEEE Transactions on Instrumentation and Measurement, 70, 2503911(2021).

[20] Kou X P, Liu S J, Cheng K Q et al. Development of a YOLO-V3-based model for detecting defects on steel strip surface[J]. Measurement, 182, 109454(2021).

[21] Li W G, Ye X, Zhao Y T et al. Strip steel surface defect detection based on improved YOLOv3 algorithm[J]. Acta Electronica Sinica, 48, 1284-1292(2020).

[22] Cheng S, Yang H G, Xu X Q et al. Improved lightweight X-ray aluminum alloy weld defects detection algorithm based on YOLOv5[J]. Chinese Journal of Lasers, 49, 2104005(2022).

[23] Cao Y Q, Wu M L, Xu L. Steel surface defect detection based on improved YOLOv5 algorithm[J]. Journal of Graphics, 44, 335-345(2023).

[24] Wang C Y, Bochkovskiy A, Liao H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C], 7464-7475(2023).

[25] Liu Z, Hu H, Lin Y T et al. Swin transformer V2: scaling up capacity and resolution[C], 11999-12009(2022).

[26] Nascimento M G, Prisacariu V, Fawcett R. DSConv: efficient convolution operator[C], 5147-5156(2019).

[27] Wang C Y, Liao H Y M, Wu Y H et al. CSPNet: a new backbone that can enhance learning capability of CNN[C], 1571-1580(2020).

[28] Wang J W, Xu C, Yang W et al. A normalized Gaussian Wasserstein distance for tiny object detection[EB/OL]. http://arxiv.org/abs/2110.13389v2

[29] Wang G H, Zheng H, Zhang X. A robust checkerboard corner detection method for camera calibration based on improved YOLOX[J]. Frontiers in Physics, 9, 819019(2022).

[30] Wang H, Yang X D, Zhou B et al. Strip surface defect detection algorithm based on YOLOv5[J]. Materials, 16, 2811(2023).

[31] Zhou X Y, Wang D Q, Krähenbühl P. Objects as points[EB/OL]. http://arxiv.org/abs/1904.07850v2

[32] Zhang Z C. Lightweight strip steel defect detection based on improved YOLOv5[J]. Computer Systems and Applications, 32, 278-285(2023).

[33] Reis D, Kupec J, Hong J et al. Real-time flying object detection with YOLOv8[EB/OL]. http://arxiv.org/abs/2305.09972v2

[34] Wang C Y, Yeh I H, Liao H Y M. YOLOv9: learning what you want to learn using programmable gradient information[EB/OL]. http://arxiv.org/abs/2402.13616v2