[1] WU H B, WEI X Y, LIU M H et al. Improved YOLOv4 for dangerous goods detection in X-ray inspection combined with atrous convolution and transfer learning[J]. Chinese Optics, 14, 1417-1425(2021).

[2] ZHOU B, LI J F. Human action recognition combined with object detection[J]. Acta Automatica Sinica, 46, 1961-1970(2020).

[3] SONG R, SHI Z P, QU Y et al. Road scene understanding for autonomous driving via deep residual learning[J]. Application Research of Computers, 36, 2825-2829, 2871(2019).

[4] HE Y T, CHE J, WU J M. Pedestrian multi-target tracking method based on YOLOv5 and person re-identification[J]. Chinese Journal of Liquid Crystals and Displays, 37, 880-890(2022).

[5] LIU B, HAN G L, LUO H Y. Image fusion algorithm based on multi-scale detail siamese convolutional neural network[J]. Chinese Journal of Liquid Crystals and Displays, 36, 1283-1293(2021).

[6] 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).

[7] HE K M, GKIOXARI G, DOLLÁR P et al. Mask R-CNN[C], 2980-2988(2017).

[8] REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C], 6517-6525(2017).

[9] REDMON J, FARHADI A. YOLOv3: an incremental improvement[J/OL]. arXiv, 1804. 02767(2018).

[10] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOV4: optimal speed and accuracy of object detection[J/OL]. arXiv, 2004. 10934(2020).

[11] LAW H, DENG J. CornerNet: detecting objects as paired keypoints[J]. International Journal of Computer Vision, 128, 642-656(2020).

[12] ZHOU X Y, WANG D Q, KRÄHENBÜHL P. Objects as points[J/OL]. arXiv, 1904. 07850(2019).

[13] GE Z, LIU S T, WANG F et al. YOLOX: exceeding YOLO series in 2021[J/OL]. arXiv, 2107. 08430(2021).

[14] HOWARD A G, ZHU M L, CHEN B et al. MobileNets: efficient convolutional neural networks for mobile vision applications[J/OL]. arXiv, 1704. 04861(2017).

[15] ZHANG X Y, ZHOU X Y, LIN M X et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices[C], 6848-6856(2018).

[16] HAN K, WANG Y H, TIAN Q et al. GhostNet: more features from cheap operations[C], 1577-1586(2020).

[17] HOWARD A, SANDLER M, CHEN B et al. Searching for MobileNetv3[C], 1314-1324(2019).

[18] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[19] 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).

[20] LIN T Y, DOLLÁR P, GIRSHICK R et al. Feature pyramid networks for object detection[C], 936-944(2017).

[21] SZEGEDY C, VANHOUCKE V, IOFFE S et al. Rethinking the inception architecture for computer vision[C], 2818-2826(2016).

[22] DENG J K, GUO J, VERVERAS E et al. RetinaFace: single-shot multi-level face localisation in the wild[C], 5202-5211(2020).

[23] ZHENG Z H, WANG P, LIU W et al. Distance-IoU loss: faster and better learning for bounding box regression[C], 12993-13000(2020).

[24] BODLA N, SINGH B, CHELLAPPA R et al. Soft-NMS-improving object detection with one line of code[C], 5562-5570(2017).

[25] GEVORGYAN Z. SIoU loss: more powerful learning for bounding box regression[J/OL]. arXiv, 2205. 12740(2022).

[26] WOO S, PARK J, LEE J Y et al. CBAM: convolutional block attention module[C], 3-19(2018).

[27] LIU S, QI L, QIN H F et al. 2018. Path aggregation network for instance segmentation[C], 8759-8768(2018).