Recognition Algorithm for UAV Ground Military Targets Based on Improved Yolov5n
In response to low recognition accuracy,high false detection rate and missed detection rate of mainstream target detec-tion algorithms in real aerial battlefield data backgrounds,research was conducted on the Yolo target recognition algorithm,and a lightweight aerial military target detection model based on improved Yolov5n was proposed;Firstly,the efficient channel attention(ECA)mechanism is integrated with the C3 module of the trunk network to solve the interference from complex backgrounds and sim-ilar targets in aerial images;Secondly,the normalized Gaussian Wasserstein distance(NWD)is introduced to replace the CIoU loss function,improving the detection and recognition of fuzzy small targets;Finally,the GSConv lightweight convolution is used to replace standard convolution to reduce the weight of the model;After experimental testing,the improved algorithm model reaches an average detection accuracy of 81.5％and improves 0.9 percentage points,with the model size of 3.4 MB,reduction of 0.4 MB,and recognition speed of 113 fps;Experimental results show that the model has high accuracy in aerial military target detection while being lightweight.
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