关键词: 目标检测；YOLOv5s；注意力机制；解耦头

Abstract: Object detection in traffic scenes is a critical technology for intelligent transportation systems and autonomous driving. To address the issues of missed detections, false alarms, and insufficient detection accuracy in multi-scale targets when using the YOLOv5s algorithm in complex traffic scenarios, this paper proposes an improved CRD-YOLOv5s model. First, a convolutional block attention module (CBAM) mechanism is introduced into the backbone network to enhance the feature extraction capability for small targets through channel and spatial attention mechanisms. Second, the spatial pyramid pooling-fast (SPPF) module is replaced with an receptive field block (RFB) module, which leverages dilated convolutions to expand the receptive field and capture global features. Additionally, the SIOU loss function is adopted to optimize the training convergence process, and a decoupled head structure is introduced to separate classification and regression tasks. The experimental results demonstrate that the improved model achieves a 0.7% increase in mAP for single-target detection tasks and a 1.2% improvement in mAP for multi-target detection tasks. These enhancements not only significantly boost the detection accuracy but also effectively mitigate occurrences of missed and false detections.

Key words: object detection; YOLOv5s; attention mechanism; decoupled head