Pedestrian detection algorithm based on computer vision

In intelligent transportation system, accurate and efficient pedestrian detection is very important to improve traffic safety. This paper proposes a new pedestrian detection algorithm based on computer vision, which integrates the advantages of three classical models, YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector) and Fast RCNN. It generates a new model, HybridCVNet, which is more accurate and responsive. By introducing feature pyramid network (FPN) to enhance multi-scale feature extraction capability, and using attention mechanism to optimize key region identification, HybridCVNet shows higher detection accuracy and robustness in complex environments. The experimental results show that compared with the basic model, HybridCVNet can significantly reduce the false alarm rate and improve the detection effect of small target pedestrians, especially in the case of low light and partial occlusion, and its superior performance has been fully verified. This research provides strong support for intelligent traffic management and autonomous driving technology in the future.