基于改进YOLOX-S的安全帽反光衣检测算法

在工业生产和交通工程中，安全帽和反光衣都是员工重要的生命安全保障。针对传统安全帽反光衣识别方法只能检测单一颜色反光衣、检测效率低的问题，提出一种基于改进YOLOX-S网络模型的安全帽反光衣检测方法。使用简化BiFPN模块替换原加强特征提取网络，提高网络对不同尺度的特征提取能力；使用Mosaic方法进行训练，提高网络在复杂场景下的检测能力；使用GIoU损失函数，进一步提高模型的识别准确率。在扩充后的安全帽反光衣数据集上实验表明，本文所提算法在保持较高推理速度的情况下，mAP达83.74%，与原YOLOX-S相比，对戴安全帽、穿反光衣和行人的检测AP值有1%~3%不等的提高，对反光衣颜色无依赖性，有效实现了快速准确的安全帽反光衣检测。

Helmet and reflective clothing detection algorithm based on improved YOLOX-S

In industrial production and traffic engineering, safety helmets and reflective clothing are important safety protection. Aiming at the problem that the traditional helmet reflective clothing recognition method can only detect single color reflective clothing and low detection efficiency, we proposed a helmet reflective clothing detection method based on improved YOLOX-S network model. The simplified BiFPN module is used to replace the original enhanced feature extraction network to improve the feature extraction ability of the network for targets with different scales. The mosaic method is used for model training to improve the detection ability of the network in complex scenes. The GIoU loss function is used to further improve the recognition accuracy of the model. Experiments on the expanded helmet reflector data set show that the proposed algorithm can achieve 83.74% map while maintaining a high detection speed. Compared with the original YOLOX-S, the detection AP of wearing helmets, reflective clothing and pedestrians is improved by 1% ~ 3%, and there is no dependence on the color of reflective clothing, which effectively realizes the rapid and accurate detection of helmets and reflective clothing.