[1] SUN X Y, YANG S S, ZHAO C. Lightweight industrial image classifier based on federated few-shot learning[J]. IEEE Transactions on Industrial Informatics, 19, 7367-7376(2023).

[2] AGGARWAL A, KUMAR V, GUPTA R. Object detection based approaches in image classification： a brief overview[C], 1-6(2023).

[3] YASMINE G, MAHA G, HICHAM M. Overview of single-stage object detection models： from yolov1 to Yolov7[C], 1579-1584(2023).

[4] [4] 庄存波， 刘检华， 隋秀峰， 等. 工业互联网推动离散制造业转型升级的发展现状、技术体系及应用挑战［J］. 计算机集成制造系统， 2019， 25（12）： 3061-3069. doi: 10.13196/j.cims.2019.12.008ZHUANGC B， LIUJ H， SUIX F， et al. Status， technical architecture and application challenges for transformation and updating of discrete manufacturing industry driven by industrial Internet［J］. Computer Integrated Manufacturing Systems， 2019， 25（12）： 3061-3069.（in Chinese）. doi: 10.13196/j.cims.2019.12.008

[5] WANG Y T, SHEN W W. Mechanical parts detection algorithm based on enhanced faster R-CNN[C], 4348-4353(2021).

[6] EVERINGHAM M, MALI ESLAMI S, VAN GOOL L et al. The pascal visual object classes challenge： a retrospective[J]. International Journal of Computer Vision, 111, 98-136(2015).

[7] LIN T Y, MAIRE M, BELONGIE S et al. Microsoft COCO： Common Objects in Context[M]. Computer Vision-ECCV 2014, 740-755(2014).

[8] KUZNETSOVA A, ALLDRIN N et al. The open images dataset V4[J]. International Journal of Computer Vision, 128, 1956-1981(2020).

[9] DENG J, DONG W, SOCHER R et al. ImageNet： a large-scale hierarchical image database[C], 248-255(2009).

[10] DROST B, ULRICH M, BERGMANN P et al. Introducing MVTec ITODD-a dataset for 3D object recognition in industry[C], 2200-2208(2017).

[11] HODAN T, HALUZA P, OBDRŽÁLEK Š et al. T-LESS： an RGB-D dataset for 6D pose estimation of texture-less objects[C], 880-888(2017).

[12] TABERNIK D, ŠELA S, SKVARČ J et al. Segmentation-based deep-learning approach for surface-defect detection[J]. Journal of Intelligent Manufacturing, 31, 759-776(2020).

[13] KHALILIAN S, HALLAJ Y, BALOUCHESTANI A et al. PCB defect detection using denoising convolutional autoencoders[C], 1-5(2020).

[14] HE Y, SONG K C, MENG Q G et al. An end-to-end steel surface defect detection approach via fusing multiple hierarchical features[J]. IEEE Transactions on Instrumentation and Measurement, 69, 1493-1504(2020).

[15] CHEN H X, DU Y Z, FU Y Q et al. DCAM-net： a rapid detection network for strip steel surface defects based on deformable convolution and attention mechanism[J]. IEEE Transactions on Instrumentation and Measurement, 72, 5005312(2023).

[16] WANG W Y, MI C F, WU Z H et al. A real-time steel surface defect detection approach with high accuracy[J]. IEEE Transactions on Instrumentation and Measurement, 71, 5005610(2021).

[17] [17] 郭峰， 朱启兵， 黄敏， 等. 基于改进YOLOV4的陶瓷基板瑕疵检测［J］. 光学 精密工程， 2022， 30（13）： 1631-1641. doi: 10.37188/OPE.20223013.1631GUOF， ZHUQ B， HUANGM， et al. Defect detection in ceramic substrate based on improved YOLOV4［J］. Opt. Precision Eng.， 2022， 30（13）： 1631-1641.（in Chinese）. doi: 10.37188/OPE.20223013.1631

[18] [18] 李宝平， 戚恒熠， 王满利， 等. 联合3D建模与改进CycleGAN的故障数据集扩增方法［J］. 光学 精密工程， 2023， 31（16）： 2406-2417. doi: 10.37188/ope.20233116.2406LIB P， QIH Y， WANGM L， et al. Equipment fault dataset amplification method combine 3D model with improved CycleGAN［J］. Opt. Precision Eng.， 2023， 31（16）： 2406-2417.（in Chinese）. doi: 10.37188/ope.20233116.2406

[19] CHEN W, ZHAO J, ZHAO W L et al. Shape-aware monocular 3D object detection[J]. IEEE Transactions on Intelligent Transportation Systems, 24, 6416-6424(2023).

[20] XIA G S, BAI X, DING J et al. DOTA： a large-scale dataset for object detection in aerial images[C], 3974-3983(2018).

[21] CHEN H, SHI Z W. A spatial-temporal attention-based method and a new dataset for remote sensing image change detection[J]. Remote Sensing, 12, 1662(2020).

[22] 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(2017).

[23] LIU W, ANGUELOV D, ERHAN D et al. SSD： Single Shot MultiBox Detector[M]. Computer Vision-ECCV 2016, 21-37(2016).

[24] CARION N, MASSA F, SYNNAEVE G et al. End-to-End object detection with transformers[M]. Computer Vision-ECCV 2020, 213-229(2020).

[25] REDMON J, FARHADI A. YOLOv3： an incremental improvement[C](2018).

[26] ULTRALYTICS. YOLOv5[webpage]. ULTRALYTICS(2021). https：//github.com/ultralytics/YOLOv5

[27] ULTRALYTICS. YOLOv8[webpage]. ULTRALYTICS(2023). https：//github.com/ultralytics/YOLOv8