YOLOv3 Pedestrian Detection Algorithm Based on Multi-scale Feature Fusion
With popularization and promotion of deep learning techniques in the field of computer,the pedestrian detection technology has been further improved,but still on several occasions there is a big problem,for example the pedestrian size differ-ence,dense pedestrian detection,in the above two cases,the pedestrian detection performance fell sharply,there exist residual sit-uation and false detection.For pedestrians size problem,YOLOv3 algorithm is introduced in the feature extraction of network multi-scale feature fusion module,changing the original multiple convolution of residual layer stack unit,increasing network depth of feature extraction and improving the network of the different scales of pedestrian feature extraction ability,so as to improve the pe-destrian detection accuracy and robustness of the algorithm.Experimental results show that the average accuracy of the improved al-gorithm is 5.49%and 2.26%higher than that of the benchmark algorithm after training in Caltech and ON_MERGE data sets.
国家科技期刊平台
NSTL
万方数据
