CamoNAS: Neural Architecture Search for Enhanced Camouflaged Object Detection
For researchers in camouflaged object detection, this work demonstrates the first successful application of NAS to this domain, outperforming hand-designed architectures.
CamoNAS introduces a frequency-aware multi-resolution NAS framework for camouflaged object detection, achieving state-of-the-art results on four benchmarks (CAMO, COD10K, NC4K, CHAMELEON).
Camouflaged Object Detection (COD) aims to locate and segment objects that blend into their surroundings, presenting challenges due to weak edge cues and ill-defined boundaries. Traditional COD models rely on hand-designed architectures and multi-scale feature fusion, which are often guided by intuition rather than systematic search. This paper introduces CamoNAS, a frequency-aware multi-resolution Neural Architecture Search (NAS) framework for COD. CamoNAS automatically searches both cell-level operations and network-level downsampling paths, forming a hierarchical search space tailored to detect camouflaged objects. Additionally, it adopts an RGB frequency dual-stream architecture, where a learnable wavelet transform complements the RGB spatial stream. CamoNAS achieves state-of-the-art performance on four COD benchmarks (CAMO, COD10K, NC4K, CHAMELEON), highlighting the effectiveness of NAS for COD. Our code is available at https://github.com/rendaweiSIMIT/CamoNAS.