A small target detection algorithm of UAV aerial photography images based on improved YOLOX
Target detection for unmanned aerial vehicles(UAVs)has great potential.However,different from natural images,aerial images taken by UAVs are more complex,containing considerable small targets.This has imposed challenges for the existing detection algorithms,since they lack the feature extraction ability for small targets,leading to serious problems of false detection and missing detection.Therefore,this paper proposes an efficient small target detection algorithm based on YOLOX framework.Firstly,a layer of feature fusion structure is added to the feature fusion network to detect small targets,and the recognition ability of the network to small targets is enhanced by using the abundant position information and contour information in the shallow feature map.,A layer of convolution layer in the head network is reduced and the number of channels is reduced,thus the increase of additional parameters is prevented.Secondly,a channel spatial attention module(CSAM)is proposed.It uses the optimal weight allocation to drive the network to focus on the small and target-dense regions in the feature graph.Finally,a position-guided label allocation strategy(LB-SimOTA)is proposed.According to the intersection ratio(IOU)between each prediction box and the real box,different weights are assigned to improve the quality of the overall prediction box in the network.Experimental results on VisDrone2019,a data set with most small targets,show that compared with those of YOLOX-S,the detection accuracy of the proposed algorithm for vehicles and people is improved by 8.63％,and the detection speed is up to 86 FPS.The proposed algorithm is more suitable for the scenario of UAV detection of small targets on the ground.
万方数据
维普
