Complex road vehicle detection based on feature fusion
During the process of vehicle detection,due to the great influence of the complex background,as well as the difficulty in extracting the features of vehicle targets and occluded targets in the distant scene,false detection and missed detection occur.Therefore,an improved vehicle detection algorithm YOLOv5-BA under the complex background of YOLOv5 is proposed.By referring to the feature fusion idea of Weighted Bidirectional Feature Pyramid Network(BiFPN),and introducing the adaptive feature fusion module ASFF in the detection part,the detection performance is improved.The experiment results show that the improved algorithm has a detection average precision of 98％on the KITTI dataset,and a detection speed of 42 FPS,and the detection accuracy is higher than the mainstream target detection algorithm.Under the requirements of real-time detection,YOLOv5-BA has better performance in complex background,far scene and occlusion situations.
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