DP-YOLO: A lightweight traffic sign detection model for small object detection

Autonomous driving is a critical area in artificial intelligence, with vast potential for development. While current object detection algorithms have shown strong performance in traffic sign detection, they still face difficulties with small object recognition, often resulting in missed or false detections. To address this, we propose DP-YOLO, a traffic sign detection algorithm based on YOLOv8s. To enhance detection accuracy for small objects and reduce the model's parameter count, we first removed the large object detection layer from the baseline model and added a small object detection layer. In the feature extraction stage, we design the DBBNCSPELAN4 module to boost the network's feature extraction capability. Additionally, we propose the PTCSP module, incorporating Transformer technology into the model's feature processing network and reducing both parameters and computational cost. Finally, we introduce the W3F_MPDIoU loss to mitigate the impact of low-quality samples on the model and enhance its robustness. Experiments demonstrate that, compared to YOLOv8s, DP-YOLO reduces the model's parameter count by 77.0%, while achieving improvements in mAP0.5 by 5.8% on the TT100K dataset, 2.7% on the GTSDB dataset, and 1.3% on the CCTSDB dataset. Experimental results demonstrate that the proposed method effectively enhances the detection capability for small-sized traffic signs and exhibits high potential for edge deployment. • Removed large target detection layers and added small target detection layers to enhance small object detection and reduce parameters. • Developed the DBBNCSPELAN4 module by integrating CSPNet, ELAN, and DBB for efficient multi-scale feature extraction. • Proposed the PTCSP module, combining CNNs and Transformers for optimized computation and feature extraction. • Designed the W3F MPDIoU loss to accelerate convergence and improve regression accuracy, focusing on hard-to-classify positives.