关键词: YOLO方法, 行人检测, 特征增强

Abstract:

Pedestrian sizes in urban road scenes vary widely, and dense crowds can easily cause occlusion problems, increasing the difficulty of pedestrian detection. In order to improve the accuracy and real-time performance of pedestrian detection in urban road areas, and better meet the actual needs of driving scenarios, the paper improves the You Only Look Once（YOLO） method. The original YOLO model is divided into a pedestrian feature extraction stage and a pedestrian coordinate regression stage. The shallow features and deep features are multi-scaled to increase the feature extraction effect of the backbone network, by attention mechanism and GIoU loss, adding spatial channel enhancement module after feature fusion to improve recognition ability of occluded targets. Using Cross YOLO layer and adjusting the width of the network in combination with the size of the pedestrian has speeded up the model calculation speed. The verification experiments are performed on the Caltech pedestrian benchmark dataset. The results show that compared with the original YOLO and other popular methods, the YOLO-Person model has a lower false detection rate for small targets and occluded targets, and it is faster and has certain practical applications. value.

Key words: You Only Look Once（YOLO）, pedestrian detection, feature enhancement