Research on Vehicle Detection Based on Deep Learning

To address the issue of low real-time performance in traditional target detection algorithms for vehicle detection and tracking, low accuracy, and susceptibility to occlusion leading to omission and mixed detection, a deep learning-based vehicle detection algorithm is proposed. The algorithm incorporates a lightweight convolutional attention module (CBAM), improves the trunk-head network and introduces adaptive spatial feature fusion (ASFF) as well as a multi-branch RFB module layer on the basis of the original YOLOv5s network model. After the introduction of this algorithm, remarkable improvements are observed in detection accuracy, real-time performance, and the mitigation of occlusion-related issues like omission and mixing, surpassing the performance of traditional network applications.