Research on small target pedestrian detection based on improved YOLO

Aiming at the problems of low detection accuracy and speed for small target pedestrian in traffic scenes, the YOLO-SP based on YOLO-v4 is proposed. Firstly, the KITTI and INRIA data sets are used to make the new data set, and k-means algorithm is used to cluster the anchor box. Secondly, in the feature fusion phase (Neck), we increase the number of fused channels and simplify the number of output channels. Finally, to optimise the loss function, GIOU is used to calculate the coordinate loss, Focal is used to calculate the confidence loss. The test shows that all the improvement measures show better effect on small and overlapping pedestrians, the final detection accuracy (AP) is increased by 4.0%, the detection speed is accelerated by 11.3%. YOLO-SP has a certain practicality in the small target pedestrian detection. Copyright © 2021 Inderscience Enterprises Ltd.