用自动搜索设计更优网络，提升隐身目标检测效果
CamoNAS: Neural Architecture Search for Enhanced Camouflaged Object Detection
- 通过频率感知的多分辨率搜索，自动优化网络结构
- 在4个数据集上达最优，显著提升隐身物体检测精度
- 适合需要高精度目标检测的研究者和工程师
隐身目标检测（COD）旨在定位并分割与背景融合的物体，因边缘线索弱、边界模糊而具挑战性。传统模型依赖人工设计架构和多尺度特征融合，常基于直觉而非系统搜索。本文提出CamoNAS，一种面向COD的频域感知多分辨率神经架构搜索框架。CamoNAS自动搜索细胞级操作与网络级下采样路径，构建分层搜索空间以适应隐身物体检测。同时采用RGB频域双流结构，引入可学习的小波变换增强空间流。CamoNAS在四个COD基准数据集（CAMO、COD10K、NC4K、CHAMELEON）上达到当前最优性能，验证了NAS在COD中的有效性。代码已开源：https://github.com/rendaweiSIMIT/CamoNAS。
原文摘要 · Abstract (English)
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.
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