SDIM-YOLOv5: Real-Time Helmet Detection Method for Non-Motorised Vehicles Based on Improved YOLOv5

Abstract

To effectively address the challenge of balancing detection accuracy and efficiency in helmet-wearing detection, this paper proposed an improved YOLOv5 framework called SDIM-YOLOv5. Firstly, the CSPDarknet53 in the YOLOv5s benchmark structure was replaced by SimRepCSP to reduce model size. Secondly, the DualConv module was introduced into the neck network of YOLOv5s to construct the C3_DualConv modules. Thirdly, MPDIoU is combined with InnerIoU to construct the InnerMPDIoU loss function for bounding-box regression. Experimental results on the custom dataset show that our proposed model SDIM-YOLOv5 could achieve about 95.5% recall and 97.2% mAP@0.5, respectively, outperforming the YOLOv5s baseline. Meanwhile, SDIM-YOLOv5 only contains 5.89 M parameters with a smaller model size of 12.1 MB compared with the YOLOv5s model. Furthermore, compared with lightweight YOLO models with comparable model complexity, SDIM-YOLOv5 demonstrates competitive detection performance. Finally, deployment experiments on an embedded device demonstrate the feasibility of SDIM-YOLOv5 for edge-based helmet-wearing detection.