YOLO-DenseFuse: A Visible-Infrared Feature Fusion Framework for Intelligent Camouflage Target Detection in Complex Environments

Camouflage target detection is one of the key challenges in the field of deception detection, especially with the continuous advancement of camouflage technologies. The high fusion of target and environmental background makes conventional single-modal object detection algorithms perform significantly worse in complex environments. To address this issue, this paper proposes a novel camouflage target intelligent detection algorithm based on visible-infrared feature-level fusion, called YOLO-DenseFuse. First, a YOLO-Dense dual-stream feature extraction channel for visible and infrared images is designed to efficiently extract and reuse information from both infrared and visible light bands. Then, to efficiently utilize the positional information in infrared images, residual connections are employed to extract target location information. Finally, a camouflage target multi-spectral dataset is constructed for performance validation of multi-modal image fusion methods. Experimental results show that the proposed method achieves a detection accuracy of 91.91% and a recall rate of 93.33%, outperforming the YOLOv7 algorithm. Compared with other mainstream object detection algorithms, the proposed method demonstrates significant advantages in detection accuracy and other evaluation metrics. This approach has significant application prospects and practical significance for camouflage target detection in complex field battle environments, contributing to enhanced battlefield situational awareness.