Integrated adaptive weighted vehicle-road cooperative foreign object detection model

Foreign object intrusion on highways is a significant cause of major traffic accidents. However, existing foreign object detection models, which prioritize detection accuracy, commonly suffer from issues such as model complexity, a large number of network parameters, and poor real-time performance. Moreover, these models are typically deployed on server-side platforms and are not suitable for the vehicle-road cooperative foreign object detection domain, which requires wide coverage and high real-time requirements. To address these challenges, this paper proposes a fusion adaptive weighted vehicle-road cooperative foreign object detection model. The model employs depth separable convolution to construct the backbone network, reducing model complexity and parameter count to enhance detection real-time performance. Attention mechanisms are introduced to capture features of local target regions, thereby improving feature extraction capability. By adaptively weighting and fusing vehicle and roadside smart lamppost detection information, detection accuracy is further enhanced. Experimental results demonstrate that the proposed foreign object detection model achieves a computational load of 7.70 GFLOPS, parameter count of 11.72 million, frame rate of 38.56 fps, and detection accuracy of 79.88%. Compared to the YOLOv4 model, the proposed model reduces parameter count by 81.8%, computational load by 87.3%, and improves real-time performance by 31.9%. As a result, it can effectively be applied in the vehicle-road cooperative domain.