SNCF workers detection in the railway environment based on improved YOLO v5

International audience In nearest past years object detection techniques becomes the magic key to solving several problems in computer vision, in this work, we introduce our enhanced YOLO v5 detector for detecting SNCF (National Society of France Railroad) workers in the railway environment. Our contribution in this work is presented by creating a new dataset about SNCF workers to use for training our model detector and improving YOLO v5 by reducing the number of its parameters where we reduce the number of classes in YOLO layers to only one class, that ensure to augment the speed of detection and increase the accuracy of our detector. Finally, we apply the four versions of YOLO v5 (S, M, L, X) and compare them. We achieved a high speed in the detection of SNCF workers in YOLO v5-S with 0.1 ms and high precision in YOLO v5-X with a rate of 0.9731 %.