FPN-Centernet Helmet Wearing Detection Algorithm

doi:10.3778/j.issn.1002-8331.2202-0181

Abstract

Abstract: As the safety guarantee of workers in the construction site, the wearing of safety helmet affects the life safety of workers. In terms of wearing detection, the introduction of deep learning can effectively remind workers to wear safety helmets. However, because the image of safety helmet in the construction image is too small, CenterNet does not perform well. Therefore, in view of this situation, FPN-CenterNet framework is proposed. Then, ACNet（asymmetric convolution kernel）is utilized to enhance the feature extraction of the backbone network. Finally, DIoU loss function is used to optimize the accuracy of frame prediction. Compared with the original CenterNet algorithm mAP, the final modified algorithm improves 4.99 percentage points, and the FPS on the GTX GeForce 1050 GPU reaches 25.81. Experimental results show that the modified algorithm has good accuracy and efficiency in helmet wearing detection.

Key words: safety helmet wearing detection, feature pyramid net, asymmetric convolution kernel, DIoU loss function

摘要： 安全帽作为施工场所工人的安全保障，佩戴与否影响着工人的生命安全。在佩戴检测方面引入深度学习可以高效地提醒工人佩戴安全帽。但由于施工图像中安全帽的图像过小，CenterNet表现得并不好。因此针对这个情况，提出了FPN-CenterNet框架；使用ACNet非对称卷积核来对主干网络的特征提取进行增强；使用DIoU损失函数来优化边框预测的准确度。最终修改的算法相较于原始的CenterNet算法mAP提升了4.99个百分点，在GTX GeForce 1050的GPU上的FPS达到25.81。实验结果表明修改之后的算法在安全帽佩戴检测上有良好的准确性和效率。

关键词: 安全帽佩戴检测, 特征金字塔, 非对称卷积核, DIoU损失函数

ZHAO Jianghe, WANG Hairui, WU Lei. FPN-Centernet Helmet Wearing Detection Algorithm[J]. Computer Engineering and Applications, 2022, 58(14): 114-120.

赵江河, 王海瑞, 吴蕾. FPN-CenterNet安全帽佩戴检测算法[J]. 计算机工程与应用, 2022, 58(14): 114-120.

References

[1] 李太华，王迪.基于安全帽颜色识别的人员身份认证算法在变电站的应用[J].电脑知识与技术，2014（5）：1018-1020.
LI T H，WANG D.The algorithm of identity authentication based on the recognition of safety helmet color that is used in substation[J].Computer Knowledge and Technology，2014（5）：1018-1020.
[2] 冯国臣，陈艳艳，陈宁，等.基于机器视觉的安全帽自动识别技术研究[J].机械设计与制造工程，2015，44（10）：39-42.
FENG G C，CHEN Y Y，CHEN N，et al.Research on automatic identification technology of the safety helmet based on machine vision[J].Mechanical Design and Manufacturing Engineering，2015，44（10）：39-42.
[3] 庞霞，王俪蓉.基于颜色和形状的安全帽识别方法[C]//2017智能电网信息化建设研讨会论文集，2017：50-51.
PANG X，WANG L R.Helmet recognition method based on color and shape[C]//Proceedings of 2017 Smart Grid Information Construction Seminar，2017：50-51.
[4] REN S，HE K，GIRSHICK R，et al.Faster R-CNN：towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems，2015.
[5] REDMON J，DIVVALA S，GIRSHICK R，et al.You only look once：unified，real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：779-788.
[6] REDMON J，FARHADI A.YOLO9000：better，faster，stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：7263-7271.
[7] REDMON J，FARHADI A.YOLOv3：an incremental improvement[J].arXiv：1804.02767，2018.
[8] HE K，GKIOXARI G，DOLLáR P，et al.Mask R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision，2017：2961-2969.
[9] FU C Y，LIU W，RANGA A，et al.DSSD：deconvolutional single shot detector[J].arXiv：1701.06659，2017.
[10] LIN T Y，GOYAL P，GIRSHICK R，et al.Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision，2017：2980-2988.
[11] ZHOU X，WANG D，KR?HENBüHL P.Objects as points[J].arXiv：1904.07850，2019.
[12] LONG X，CUI W，ZHENG Z.Safety helmet wearing detection based on deep learning[C]//2019 IEEE 3rd Information Technology，Networking，Electronic and Automation Control Conference（ITNEC），2019：2495-2499.
[13] GU Y，XU S，WANG Y，et al.An advanced deep learning approach for safety helmet wearing detection[C]//2019 International Conference on Internet of Things（iThings） and IEEE Green Computing and Communications（GreenCom） and IEEE Cyber，Physical and Social Computing（CPSCom） and IEEE Smart Data（SmartData），2019：669-674.
[14] LI N，LYU X，XU S，et al.Incorporate online hard example mining and multi-part combination into automatic safety helmet wearing detection[J].IEEE Access，2020，9：139536-139543.
[15] SHEN J，XIONG X，LI Y，et al.Detecting safety helmet wearing on construction sites with bounding box regression and deep transfer learning[J].Computer Aided Civil and Infrastructure Engineering，2021，36（2）：180-196.
[16] 方钧婷，谭晓阳.注意力级联网络的金属表面缺陷检测算法[J].计算机科学与探索，2021，15（7）：1245-1254.
FANG J T，TAN X Y.Defect detection of metal surface based on attention cascade R-CNN[J].Journal of Frontiers of Computer Science and Technology，2021，15（7）：1245-1254.
[17] 赵鹏飞，谢林柏，彭力.融合注意力机制的深层次小目标检测算法[J].计算机科学与探索，2022，16（4）：927-937.
ZHAO P F，XIE L B，PENG L.Deep small object detection algorithm integrating attention mechanism[J].Journal of Frontiers of Computer Science and Technology，2022，16（4）：927-937.
[18] 宋艳艳，谭励，马子豪，等.改进YOLOV3算法的视频目标检测[J].计算机科学与探索，2021，15（1）：163-172.
SONG Y Y，TAN L，MA Z H，et al.Video target detection based on improved YOLOV3 algorithm[J].Journal of Frontiers of Computer Science and Technology，2021，15（1）：163-172.
[19] LIN T Y，DOLLáR P，GIRSHICK R，et al.Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：2117-2125.
[20] DING X，GUO Y，DING G，et al.Acnet：strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2019：1911-1920.
[21] ZHENG Z，WANG P，LIU W，et al.Distance-IoU loss：faster and better learning for bounding box regression[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2020：12993-13000.