[1] [1] Nath N D, Behzadan A H, Paal S G. Deep learning for site safety: Real-time detection of personal protective equipment［J］. Automation in Construction, 2020, 112: 103085.

[2] [2] Wang Z, Wu Y, Yang L, et al. Fast personal protective equipment detection for real construction sites using deep learning approaches［J］. Sensors, 2021, 21(10): 3478.

[3] [3] Li T, Chen Q, Gan J, et al. Investigating into deep neural networks for applicable hard-hat wearing detection in substations［J］. J. of Physics: Conf. Series, 2021, 2002(1): 012062.

[4] [4] Fu Chuan, Wang Rongxin. Research on safety helmet wearing YOLO-V3 detection technology improvement in mine environment［J］. J. of Physics: Conf. Series, 2019, 1345(4): 042045.

[5] [5] Wang X, Niu D, Luo P, et al. A safety helmet and protective clothing detection method based on improved-Yolo V3［C］// 2020 Chinese Automation Congress (CAC), 2020: 5437-5441.

[6] [6] Redmon J, Farhadi A. Yolov3: An incremental improvement［DB/OL］. (2018-4-8). http://arXiv.org/pdf/1804.02767.

[7] [7] Zhuo N, Yun G. Research on application of target detection network based on SSD in underground coal mine［C］// 2021 5th Inter. Conf. on Computer Science and Artificial Intelligence, 2021: 21-26.

[8] [8] Li X, Wang S, Liu B, et al. Improved YOLOv4 network using infrared images for personnel detection in coal mines［J］. J. of Electronic Imaging, 2022, 31(1): 013017.

[9] [9] Yang L, Zhang R Y, Li L, et al. Simam: A simple, parameter-free attention module for convolutional neural networks［C］// Inter. Conf. on Machine Learning, 2021: 11863-11874.

[10] [10] Wang X, Zhang S, Yu Z, et al. Scale-equalizing pyramid convolution for object detection［C］// Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, 2020: 13359-13368.

[11] [11] Gevorgyan Z. SIoU loss: More powerful learning for bounding box regression［DB/OL］. 2022-5-25. http:// arXiv.org/ftp/arXiv/papers/2205/2205.12740.

[12] [12] Wang C Y, Liao H Y M, Wu Y H, et al. CSPNet: A new backbone that can enhance learning capability of CNN［C］// Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops, 2020: 390-391.

[14] [14] Hu J, Shen L, Sun G. Squeeze-and-excitation networks［C］// Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2018: 7132-7141.

[15] [15] Woo S, Park J, Lee J Y, et al. Cbam: Convolutional block attention module［C］// Proc. of the European Conf. on Computer Vision (ECCV), 2018: 3-19.

[16] [16] Wang Q, Wu B, Zhu P, et al. ECA-Net: Efficient channel attention for deep convolutional neural networks［C］// Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2020: 11531-11539.

[17] [17] Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design［C］// Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, 2021: 13713-13722.

[18] [18] Lin T Y, Dollr P, Girshick R, et al. Feature pyramid networks for object detection［C］// Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2017: 2117-2125.

[19] [19] Liu S, Qi L, Qin H, et al. Path aggregation network for instance segmentation［C］// Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2018: 8759-8768.

[20] [20] Liu S, Huang D, Wang Y. Learning spatial fusion for single-shot object detection［DB/OL］. 2019-10-21. http://arXiv.org/pdf/1911.09516.

[21] [21] Ghiasi G, Lin T Y, Le Q V. Nas-fpn: Learning scalable feature pyramid architecture for object detection［C］// Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, 2019: 7036-7045.

[22] [22] Tan M, Pang R, Le Q V. Efficientdet: Scalable and efficient object detection［C］// Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, 2020: 10781-10790.

[23] [23] Dai J, Qi H, Xiong Y, et al. Deformable convolutional networks［C］//Proc. of the IEEE Inter. Conf. on Computer Vision, 2017: 764-773.

[24] [24] Zheng Z, Wang P, Ren D, et al. Enhancing geometric factors in model learning and inference for object detection and instance segmentation［J］. IEEE Trans. on Cybernetics, 2021, 52(8): 8574-8586.