A Camouflaged Target Detection Method with Improved YOLOv5 Algorithm
To comprehensively explore the information content of camouflaged target features,leverage the potential of target detection algorithms,and address issues such as low camouflage target detection accuracy and high false positive rates,a camouflage target detection algorithm named CAFM-YOLOv5(Cross Attention Fusion Module Based on YOLOv5)is proposed.Firstly,a camouflaged target multispectral dataset is constructed for the performance validation of the multimodal image fusion method;secondly,a dual-stream convolution channel is constructed for visible and infrared image feature extraction;and finally,a cross-attention fusion module is proposed based on the channel-attention mechanism and spatial-attention mechanism in order to realise the effective fusion of two different features.Experimental results demonstrate that the model achieves a detection accuracy of 96.4%and a recognition probability of 88.1%,surpassing the YOLOv5 baseline network.Moreover,when compared with unimodal detection algorithms like YOLOv8 and multimodal detection algorithms such as SLBAF-Net,the proposed algorithm exhibits superior performance in detection accuracy metrics.These findings highlight the practical value of the proposed method for military target detection on the battlefield,enhancing situational awareness capabilities significantly.