Abstract: Camouflaged object detection(COD) plays a crucial role in natural scene analysis and security monitoring.However,the complexity and diversity of camouflaged objects pose significant challenges to the performance of detection models.Existing knowledge distillation methods are primarily used for model compression by aligning the output features of teacher and student networks to achieve lightweight models.Nonetheless,these methods often overlook the rich semantic information contained in the intermediate features of teacher networks.Additionally,fixed learning rate strategies struggle to adapt to the significant scale differences between teacher and student models,leading to instability during the distillation process.To address these issues,this paper proposes a lightweight camouflaged object detection model based on structured knowledge distillation.The method leverages structured knowledge to improve the traditional soft and hard label loss calculation,significantly enhancing the distillation performance.Furthermore,the learning rate optimization problem is modeled as an optimization task to stabilize performance fluctuations during the distillation process.Experimental results demonstrate that the proposed method achieves an S_m of 82.9% and 81.0% on the COD10K-V3 and CAMO camouflaged object detection datasets,respectively,while reducing training time to 6.5 hours.

Key words: Knowledge distillation, Camouflaged object detection, Object detection, Structured knowledge, Learning rate