An improved algorithm based on YOLOv5 is proposed to address the problem of poor detection due to complex image background and too small target under UAV vision.Firstly,the algorithm proposes a Filter Separation Feature Extraction (FSFE) structure,which inputs the filter separated image into the neural network in parallel with the original image,strengthens the networks extraction of important information both globally and in detail,the output feature map is spatially adaptively fused to prevent the problem of semantic information fragmentation during fusion,and enables the network to pay more attention to the information of key layers.Secondly,a small target detection layer is added,and the SPD convolution module is utilized to enhance feature learning to improve detection performance.Finally,the CA feature enhancement module is embedded in the C3 module to mine and preserve important semantic information during feature extraction.Experimental results based on the VisDrone 2019 dataset show that mAP@0.5 and mAP@0.5∶0.95 of the improved algorithm increases by 8.3 and 6.1 percentage points respectively,and the accuracy and recall increases by 5.1 and 4.5 percentage points respectively,improving the precision of small target detection and reducing the probability of missed and false detection,which is significant for realizing the UAV visual small target detection.