A camouflage target detection method based on local minimum difference constraints

To address the problems of missing inside and incomplete edge contours in camouflaged target detection results,we propose a camouflaged moving target detection algorithm based on local minimum difference constraints(LMDC).The algorithm first uses the mean to optimize the initial background model,removes the stable background region by global comparison,and extracts the edge point set in the potential target region so that each boundary point(seed)grows along the center of the target.Finally,we define the minor difference constraints term,combine the seed path and the target space consistency,and calculate the attributes of each pixel in the potential target area to realize camouflaged moving target detection.The algorithm of this paper is verified based on a public data sofa video and test videos and compared with the five classic algorithms.The experimental results show that the proposed algorithm yields good results based on integrity,accuracy,and a number of objective evaluation indexes,and its overall performance is better than that of the compared algorithms.

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

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 of92.5%,a parameter number increase of 1×10^(4),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.