CamoNAS: Neural Architecture Search for Enhanced Camouflaged Object Detection
Summary
Introduces CamoNAS, a frequency-aware multi-resolution Neural Architecture Search framework for camouflaged object detection, achieving state-of-the-art results on four benchmarks.
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# CamoNAS: Neural Architecture Search for Enhanced Camouflaged Object Detection Source: [https://arxiv.org/abs/2607.01870](https://arxiv.org/abs/2607.01870) [View PDF](https://arxiv.org/pdf/2607.01870) > Abstract: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[this https URL](https://github.com/rendaweiSIMIT/CamoNAS)\. ## Submission history From: Dawei Ren \[[view email](https://arxiv.org/show-email/c4d619d8/2607.01870)\] **\[v1\]**Thu, 2 Jul 2026 08:25:51 UTC \(3,715 KB\)
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