PMSA-YOLO: lightweight vehicle detection with parallel multi-scale aggregation and attention mechanism

With the rapid development of smart cities, vehicle detection algorithms have become a crucial component of intelligent transportation systems, widely applied in areas such as parking detection, traffic flow analysis, and illegal intrusion detection. However, due to limitations in computational resources and the increasing complexity of traffic scenarios, vehicle detection algorithms face considerable performance requirements and a demand for lightweight models. Therefore, we propose a lightweight vehicle detection model that incorporates a parallel multi-scale aggregation (PMSA) module into an improved You Only Look Once (YOLO) v5 architecture, termed PMSA-YOLO. First, the backbone network is optimized by designing a PMSA, which not only captures multi-scale information but also reduces reliance on network depth and width while maintaining recognition efficiency. Second, a convolutional block attention module is added to the last layer of the backbone network to enhance the extraction of key features, thereby improving feature recognition efficiency. Finally, a bidirectional feature pyramid network module is integrated into the neck network, and a more streamlined PMSA module is adopted for feature fusion. Experimental results demonstrate that relative to the original model, the improved model reduces the parameter count and computational load by 29.7% and 32.9%, respectively, while increasing precision, recall, mAP50, and mAP50–95 by 4.5%, 3.4%, 4.4%, and 2.6%, respectively.