基于可见光-红外特征级融合的低光照下伪装目标智能检测技术OA

Camouflaged targets detection in low-light environments is one of the challenges in the field of deception detection.Especially with the continuous advancement of camouflaged technology,targets are highly integrated with their en-vironmental background.Poor lighting conditions can often lead to performance degradation in conventional single-modal de-tection algorithms.To address this issue,this paper proposes a feature-level fusion network guided by the object detection task.First,this paper designs a residual dense connection to extract and stack information from multiple dimensions,enhan-cing the prominence of the target within the original information to obtain fused features of camouflaged targets.Then,the fused features are fed into the YOLOv7 network for camouflaged target detection.By optimizing the loss function and integra-ting spatial-channel attention mechanisms,the detection performance of camouflaged targets under low-light conditions is ef-fectively improved.Additionally,this paper constructs an optical-infrared camouflaged target dataset for low-light environ-ments to validate the proposed method with empirical data.The dataset shows an mAP@0.5 of 87.38%and a precision(P)of 85.45%,indicating that the proposed algorithm has a detection advantage for camouflaged targets under low-light condi-tions.