A Review of Deep Learning-Based Cross-Domain Fusion Methods for Camouflaged Object Detection

Camouflage object detection, as a research hotspot in the field of computer vision, not only holds significant theoretical research value but also boasts broad practical application prospects. In complex scenarios, this task often encounters performance bottlenecks such as low detection accuracy and insufficient robustness. Cross-domain fusion technology, by integrating multi-source complementary information, has become a core and key approach to breaking through these bottlenecks and enhancing the model's perception capabilities. Therefore, this paper conducts a comprehensive and systematic review of cross-domain fusion technology for camouflage object detection based on deep learning. Firstly, a multi-dimensional classification framework is proposed, systematically categorizing existing research efforts into three core categories: cross-modal data fusion, cross-network architecture fusion, and cross-task fusion, clearly defining the technical boundaries and core positions of each category. Subsequently, the core ideas, mainstream implementation methods of each category are deeply analyzed, and the advantages and limitations of various methods are compared and analyzed. Finally, the core common challenges currently faced in this field are summarized, further indicating the overall trend and specific research directions of cross-domain fusion towards intelligence, engineering, and lightweight. This paper aims to provide a clear technical development context and comprehensive method reference for subsequent research in the field of cross-domain fusion for camouflage object detection, facilitating the efficient development and engineering application of the technology in this field.