PDF(Pubmed)
Abstract：
The vigorous development of the construction industry has also brought unprecedented safety risks. The wearing of safety helmets at the construction site can effectively reduce casualties. As a result, this paper suggests employing a deep learning-based approach for the real-time detection of safety helmet usage among construction workers. Based on the selected YOLOv5s network through experiments, this paper analyzes its training results. Considering its poor detection effect on small objects and occluded objects. Therefore, multiple attention mechanisms are used to improve the YOLOv5s network, the feature pyramid network is improved into a BiFPN bidirectional feature pyramid network, and the post-processing method NMS is improved into Soft-NMS. Based on the above-improved method, the loss function is improved to enhance the convergence speed of the model and improve the detection speed. We propose a network model called BiFEL-YOLOv5s, which combines the BiFPN network and Focal-EIoU Loss to improve YOLOv5s. The average precision of the model is increased by 0.9% the recall rate is increased by 2.8%, and the detection speed of the model does not decrease too much. It is better suited for real-time safety helmet object detection, addressing the requirements of helmet detection across various work scenarios.
摘要：
建筑业的蓬勃发展也带来了前所未有的安全隐患。施工现场佩戴安全帽可以有效减少人员伤亡。因此,本文建议采用基于深度学习的方法来实时检测建筑工人的安全帽使用情况。本文通过实验选取了YOLOv5s网络,本文分析了其训练效果。考虑其对小物体和遮挡物体的检测效果较差。因此,多种注意力机制用于改进YOLOV5S网络，将特征金字塔网络改进为BiFPN双向特征金字塔网络，并将后处理方法NMS改进为Soft-NMS。在上述改进方法的基础上,改进了损失函数,提高了模型的收敛速度,提高了检测速度。我们提出了一个名为BiFEL-YOLOv5s的网络模型，它结合了BiFPN网络和Focal-EIoU损耗来改进YOLOv5。模型的平均精度提高了0.9%，召回率提高了2.8%，模型的检测速度不会下降太多。它更适合实时安全头盔对象检测，满足各种工作场景中头盔检测的要求。
