Research on Vehicle and Pedestrian Detection Based on Improved RT-DETR

This paper proposes a vehicle and pedestrian detection model based on an improved RT-DETR to address the issues of high redundancy in feature extraction and insufficient accuracy for small targets in existing real-time detection models, especially in complicated traffic scenarios. The core of this improved model is to embed a parameter free SimAM (Simple Attention Module) attention mechanism in the backbone network. The SimAM mechanism dynamically generates three-dimensional attention weights through energy functions, effectively enhancing the expression ability of fine-grained features of pedestrians and vehicles. This improvement not only reduces redundant information in the feature extraction process, but also improves the detection accuracy of the model for small targets, enabling the model to more accurately identify and locate small targets when dealing with complex traffic scenes. The experimental results show that on the BDD100K dataset, the improved model achieved an average precision of 73.6%, which is 3.7 percentage points higher than the original RT-DETR, effectively enhancing the model's capability to detect vehicles and pedestrians in intricate environments.