[1] Li Z M, Cheng L, Zhu D M et al. Deep learning and spatial analysis based port detection[J]. Laser & Optoelectronics Progress, 58, 2028002(2021).

[2] Zhang C G, Xiong B L, Kuang G Y. A survey of ship detection in optical satellite remote sensing images[J]. Chinese Journal of Radio Science, 35, 637-647(2020).

[3] Girshick R, Donahue J, Darrell T et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C], 580-587(2014).

[4] Girshick R. Fast R-CNN[C], 1440-1448(2015).

[5] 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).

[6] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016).

[7] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C], 6517-6525(2017).

[8] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[EB/OL]. https://arxiv.org/abs/1512.02325

[9] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. https://arxiv.org/abs/1804.02767

[10] Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. https://arxiv.org/abs/1804.00276

[12] Wang P, Xin X J, Wang L Q et al. Object detection algorithm of optical remote sensing images based on YOLOv3[J]. Laser & Optoelectronics Progress, 58, 2028006(2021).

[13] Xu Z J, Ding Y. Ship object detection of remote sensing images based on adaptive rotation region proposal network[J]. Laser & Optoelectronics Progress, 57, 242805(2020).

[14] Liu S, Qi L, Qin H F et al. Path aggregation network for instance segmentation[C], 8759-8768(2018).

[15] Liu Z K, Wang H Z, Weng L B et al. Ship rotated bounding box space for ship extraction from high-resolution optical satellite images with complex backgrounds[J]. IEEE Geoscience and Remote Sensing Letters, 13, 1074-1078(2016).

[16] Wang Q L, Wu B G, Zhu P F et al. ECA-net: efficient channel attention for deep convolutional neural networks[C], 11531-11539(2020).

[17] Tan M X, Pang R M, Le Q V. EfficientDet: scalable and efficient object detection[C], 10778-10787(2020).

[18] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11211, 3-19(2018).

[19] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

[20] Jiang H Y. Multi-defect detection of insulators in transmission lines for UAV aerial photography based on YOLOV5[D](2021).

[21] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection[C], 5936-5944(2017).

[22] Hao S, Yang L, Ma X et al. YOLOv5 transmission line fault detection based on attention mechanism and cross-scale feature fusion[J/OL]. Proceedings of the CSEE, 1-12. http://kns.cnki.net/kcms/detail/11.2107.tm.20220126.1718.008.html

[23] Li W G, Yang C, Jiang L et al. Indoor scene object detection based on improved YOLOv4 algorithm[J]. Laster & Optoelectronics Progress, 59, 1815003(2022).