基于知识蒸馏与轻量化模型的伪装目标检测OA

Camouflaged object detection(COD)aims to accurately identify objects concealed within complex back-grounds and is a critical factor affecting the performance of computer vision systems.It has significant application value in intelligent security surveillance,environmental perception,and smart monitoring.With the rapid develop-ment of deep learning,deep neural network(DNN)-based COD algorithms have achieved remarkable performance improvements.However,existing DNN-based COD methods typically rely on models with a large number of param-eters,leading to low inference efficiency and high computational cost.To address these challenges,this study pro-poses EfficientCOD,a highly efficient and lightweight COD framework.The core lightweight modules of Efficient-COD include:(1)the cross-scale asymmetric depthwise convolutional shuffle-selection module(CS-ADCS2M),which enables efficient cross-scale feature interaction with a reduced number of parameters,thereby enhancing the model's capability to represent camouflaged objects at varying scales;and(2)the hierarchical multi-head asymmet-ric depthwise convolutional shuffle-selection module(HMH-ADCS2M),which facilitates hierarchical context encod-ing and semantic fusion during decoding,strengthening boundary discrimination and fine-grained texture perception between camouflaged objects and their surroundings.Moreover,because lightweight models generally have weaker feature representation capability than large-capacity models,we further design a structure-aware and semantic-consistent distillation framework for COD(SaScD-COD).Using the high-capacity MambaCOD as the teacher net-work,SaScD-COD performs multi-stage knowledge transfer from fine-grained structural cues to high-level semantic representations.This enables EfficientCOD to inherit the strong representation ability of MambaCOD without increas-ing inference cost,thereby achieving a superior balance between detection accuracy and model efficiency.Extensive experiments on the CHAMELEON,CAMO,COD10K,and NC4K benchmarks demonstrate that EfficientCOD achieves comparable performance to the 47.8M-parameter teacher model MambaCOD while using only 31.4%(15.01M)of its parameters,delivering strong detection accuracy and computational efficiency.These results establish Efficient-COD as a new paradigm for efficient COD.