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.