Intelligent camouflaged target detection based on information of aerospace unmanned platforms

Intelligent camouflage target detection aims to identify targets hidden in camouflage environments using limited feature information. Existing camouflage target detection methods have problems such as low saliency features, small inter-class differences, high annotation costs for datasets, limited means of extracting small pixel features, and poor performance in extracting key features of targets in camouflage scenarios. To address these issues, this paper proposes a new intelligent camouflage target detection method based on an auxiliary information of unmanned aerospace platforms. Firstly, an improved deep convolutional generative adversarial network is proposed to expand the dataset, and enhance the image generation quality by designing a new loss function and embedding an attention mechanism module. Then, an improved Yolov11 network-Yolov11-Codattention is designed. The uniqueness of this network lies in its use of auxiliary information from space---unmanned aerospace platforms and the integration of two types of modules: context information modules and receptive field mechanism modules. These modules help solve problems such as low saliency features and small inter-class differences. By introducing these two types of modules, the designed network significantly enhances the target feature extraction capability while reducing the demand for model parameters. Based on the public camouflage dataset, camouflage target detection simulation experiments were conducted. The experimental results show that the proposed Yolov11-Codattention algorithm improves the recall rate and mAP@50 performance indicators by 7.0% and 4.8% respectively compared with the traditional Yolov11 algorithm, and the real-time performance reaches 40FPS. Through comparison with seven commonly used target detection algorithms, it is found that Yolov11-Codattention has higher camouflage target detection accuracy. The results of the field embedded deployment experiments show that the average confidence of Yolov11-Codattention reaches 0.63, and the real-time performance reaches 30FPS, meeting the engineering application requirements. The experimental results fully verify the effectiveness of the designed Yolov11-Codattention algorithm in the camouflage target detection task.