Research on Object Detection Optimization Method for Complex Traffic Environment

In complex and ever-changing traffic environments, target detection tasks require extremely high accuracy and efficiency. However, current methods still have obvious limitations in their ability to focus on key areas and the detection performance of multi-scale targets. In view of this, this paper proposes an innovative target detection model for complex traffic scenes. Specifically, the two-layer routing attention mechanism significantly enhances the model's ability to identify and detect targets by accurately focusing on key areas and effectively suppressing the interference of background noise. At the same time, the context information module combined with feature fusion integrates multi-level features, especially the fusion of low-level and high-level features, and introduces environmental context information, which significantly improves the detection accuracy of multi-scale targets in complex traffic scenes. In order to solve the problem that tiny targets are easily missed, this paper further increases the use of shallow features to improve the accuracy of tiny target detection, which significantly improves the detection accuracy of small targets. In order to verify the effectiveness of the proposed model, we conducted detailed experimental verification on the BDD100K dataset. Experimental results show that the mAP@50 index of the model has increased by 10.8%, and the mAP@50:95 index has also increased by 6.3%, while achieving high-speed real-time detection at 88.4 frames per second. This achievement fully proves that the model has successfully achieved dual optimization of accuracy and speed in complex traffic scenes, and its performance is better than other existing methods. It has broad practical application prospects, especially in the fields of intelligent transportation and autonomous driving.