Remote sensing target detection algorithm based on improved YOLOv5s
In order to solve the problem of difficult detection and low recognition accuracy of remote sensing images,a remote sensing target detection algorithm based on improved YOLOv5s is proposed.Swin trans-former module is embedded in the feature extraction network to realize the relationship modeling between the target and the scene and reduce the phenomenon of false detection.An enlarged receptive field module is designed to enlarge the receptive field of the feature map.The multi-scale feature fusion is introduced in the manner of jump connection to enhance the adaptability of the algorithm to the large-scale change of the target scale and improve the fusion efficiency.Some standand convolution in Neck part is replaced with de-formable convolution to enhance the ability of extracting the target's own region and edge features.On DI-OR data set,the effectiveness of each improvement is proved by ablation experiment.The average accuracy of mAP0.5 of the proposed algorithm is 3.62％higher than that of the original model,effectively improving the detection and recognition accuracy of remote sensing targets,which proves the effectiveness of the im-proved YOLOv5s algorithm.It can provide a basis for solving the problem of false detection and missing detection of remote sensing targets.
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