[4] CARION N, MASSA F, SYNNAEVE G, et al.End-to-end object detection with transformers［R］.Los Alamos: arXiv Preprint, 2020:arXiv:2005.12872.

[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, 2017, 39(6):1137-1149.

[6] REDMON J, DIVVALA S K, GIRSHICK R, et al.You only look once:unified, real-time object detection［C］//IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Las Vegas:IEEE, 2016:779-788.

[9] REDMON J, FARHADI A.YOLOv3:an incremental improvement［R］.Los Alamos: arXiv Preprint, 2018:arXiv1804.02767.

[11] XU D Q, WU Y Q.Improved YOLO-V3 with DenseNet for multi-scale remote sensing target detection［J］.Sensors, 2020, 20(15):4276.

[12] HUANG G, LIU Z, LAURENS V D M, et al.Densely connected convolutional networks［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2017:4700-4708.

[13] BOCHKOVSKIY A, WANG C Y, LIAO H Y.YOLOv4:optimal speed and accuracy of object detection［R］.Los Alamos: arXiv Preprint, 2020:arXiv2004.10934.

[14] LIU S, QI L, QIN H F, et al.Path aggregation network for instance segmentation［C］//IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE, 2018:8759-8768.

[15] HU J, SHEN L, SUN G．Squeeze-and-excitation networks［C］//IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE, 2018:7132-7141.

[16] HAN K, WANG Y H, TIAN Q, et al.GhostNet:more features from cheap operations［C］//IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Seattle: IEEE, 2020:1577-1586.

[17] LI Y H, YAO T, PAN Y W, et al.Contextual transformer networks for visual recognitional［R］.Los Alamos: arXiv Preprint, 2021:arXiv:2107.12292.

[19] AGGARWAL S, SINGH P.Cuckoo, bat and krill herd based k-means++ clustering algorithms［J］.Cluster Computing, 2018, 22(6):14169-14180.