BES-Net: A Complex Road Vehicle Detection Algorithm Based on Multi-Head Self-Attention Mechanism

Vehicle detection is a crucial aspect of intelligent transportation systems (ITS) and autonomous driving technologies. The complexity and diversity of real-world road environments, coupled with traffic congestion, pose significant challenges to the accuracy and real-time performance of vehicle detection models. To address these challenges, this paper introduces a fast and accurate vehicle detection algorithm named BES-Net. Firstly, the BoTNet module is integrated into the backbone network to bolster the model’s long-distance dependency, address the complexities and diversity of road environments, and accelerate the detection speed of the BES-Net network. Secondly, to accommodate the varying sizes of target vehicles, the efficient multi-scale attention mechanism (EMA) was added to enrich feature information and enhance the model’s expressive power by combining features from multiple scales. Finally, the Slide loss function is specifically designed to give higher weight to difficult samples, thereby improving the detection accuracy of small targets. The experimental results demonstrate that BES-Net achieves superior performance compared to conventional object detection models, with mAP50 (mean Average Precision), precision, and recall reaching 73.2%, 74.8%, and 73.1%, respectively. These metrics represent significant improvements of 8.5%, 7.2%, and 11.7% over the baseline network. This significant improvement underscores the high robustness of the BES-Net model in vehicle detection tasks. In addition, the BES-Net network has been deployed on NVIDIA Jetson Orin NX equipment, providing a solid foundation for its integration into intelligent transportation systems. This deployment not only showcases the practicality of the model but also highlights its potential for real-world applications in autonomous driving and ITS.