A Multi-Scale Attention-Based Pedestrian Detection Method for Roadways Using the YOLOv5 Framework

Due to multi-scale variations and occlusion problems, accurate traffic road pedestrian detection faces great challenges. This paper proposes an improved pedestrian detection method called Multi Scales Attention-YOLOv5x (MSA-YOLOv5x) based on the YOLOv5x framework. Firstly, by replacing the first convolutional operation of the backbone network with the Focus module, this method expands the number of image input channels to enhance feature expressiveness. Secondly, we construct C3_CBAM module instead of the original C3 module for better feature fusion. In this way, the learning process could achieve more multi-scale features and occluded pedestrian target features through channel attention and spatial attention. Additionally, a new feature pyramid detection layer and a new detection channel are embedded in the feature fusion part for enhancing multi-scale pedestrian detection accuracy. Compared with the baseline methods, experimental results on a public dataset demonstrate that the proposed method achieves optimal detection accuracy for traffic road pedestrian detection.