Research on Automatic Driving Road Object Detection Algorithm Integrating Multi Scale Detection and Boundary Box Regression Optimization

Pedestrian detection plays a crucial role in the driving system of automobiles, directly affecting driving safety and comfort. However, in complex driving environments, pedestrian detection faces many challenges, such as scale changes caused by distance differences between pedestrians and cameras, lighting variations, and background interference. In crowded scenes, pedestrians are often obstructed by other objects, leading to false alarms and missed detections. To address these challenges, this study improved pedestrian detection technology based on deep learning. The main contributions include building a pedestrian dataset suitable for various driving scenarios and expanding nighttime image data according to dataset requirements to enhance diversity. A multi-scale feature fusion method has been proposed to address the issue of missed detections in small-scale pedestrian SSD algorithms. Optimized the input method and adjusted the previous box size to improve the detection accuracy for small-scale pedestrians. Redundant deep features are also removed to improve detection efficiency. A YOLOX Tiny based integrated attention mechanism algorithm has been proposed. By introducing a channel attention module to enhance attention to occluded areas and using an IoU based bounding box regression loss function to improve pedestrian localization accuracy, this algorithm also reduces computational burden. The experimental results show that the algorithm effectively reduces missed detections and false positives while improving detection accuracy, achieving good performance in complex environments.