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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2300503


Camouflaged target detection based on multimodal image input pixel-level fusion


Author(s):  Ruihui PENG, Jie LAI, Xueting YANG, Dianxing SUN, Shuncheng TAN, Yingjuan SONG, Wei GUO

Affiliation(s):  Qingdao Innovation and Development Center of Harbin Engineering University, Qingdao 266000, china; more

Corresponding email(s):   laijie@hrbeu.edu.cn

Key Words:  Camouflaged target detection, Pixel-level fuse, Anchor box optimization, Loss function, Multispectral dataset


Ruihui PENG, Jie LAI, Xueting YANG, Dianxing SUN,Shuncheng TAN, Yingjuan SONG, Wei GUO. Camouflaged target detection based on multimodal image input pixel-level fusion[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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author="Ruihui PENG, Jie LAI, Xueting YANG, Dianxing SUN,Shuncheng TAN, Yingjuan SONG, Wei GUO",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300503"
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A1 - Yingjuan SONG
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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 only use 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 utilized 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. The experimental results show that the proposed method has a detection accuracy of 96.5%, a recognition probability of 92.5%, a parameter quantity of the model of 0.01 M, a theoretical calculation amount of 0.03GFLOPs, and a comprehensive detection index of the camouflage target of 0.85, which has the best comprehensive detection performance. The absolute advantage of this method in detecting accuracy is also apparent in performance comparisons with other target algorithms.

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