| Summary: | Pedestrian detection in urban traffic environment is an important field of driverless vehicle research. Due to the variability of traffic flow, target detection algorithm cannot extract complete feature information, which brings great challenges to driverless pedestrian detection. Target detection algorithm YOLOv4 has excellent detection performance in object detection, but it is not perfect in identifying semi-blocked pedestrians. In this paper, the Spatial Pyramid Pooling was added in front of the third yolo detection head module of YOLOv4 to optimize the extraction of deep network features. Then, on the basis of optimizing the network, pruning strategy was adopted to simplify the target detection algorithm, which was called TidyYOLOv4.TidyYOLOv4 and YOLOv4 (network set input image size is 864×864) were compared on the self-made human head data set. Total BFLOPS decreased by 95.04% and Inference time decreased by 82.82%. The above experimental results show that the optimized TidyYOLOv4 algorithm is more suitable for driverless pedestrian detection in urban traffic environment.
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