Abstract: Camouflaged targets are a type of nonsalient target with high foreground and background fusion and minimal target feature information, making target recognition extremely difficult. Most detection algorithms for camouflaged targets use only the target’s single-band information, resulting in low detection accuracy and a high missed detection rate. We present a multimodal image fusion camouflaged target detection technique (MIF-YOLOv5) in this paper. First, we provide a multimodal image input to achieve pixel-level fusion of the camouflaged target’s optical and infrared images to improve the effective feature information of the camouflaged target. Second, a loss function is created, and the K-Means++ clustering technique is used to optimize the target anchor frame in the dataset to increase camouflage personnel detection accuracy and robustness. Finally, a comprehensive detection index of camouflaged targets is proposed to compare the overall effectiveness of various approaches. More crucially, we create a multispectral camouflage target dataset to test the suggested technique. Experimental results show that the proposed method has the best comprehensive detection performance, with a detection accuracy of 96.5%, a recognition probability of 92.5%, a parameter number increase of 1×10⁴, a theoretical calculation amount increase of 0.03 GFLOPs, and a comprehensive detection index of 0.85. The advantage of this method in terms of detection accuracy is also apparent in performance comparisons with other target algorithms.

Key words: Camouflaged target detection; Pixel-level fusion; Anchor box optimization; Loss function; Multispectral dataset

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