构建首个恶劣天气下显著物体检测基准，提升模型在真实复杂环境中的鲁棒性。
WXSOD: A Benchmark for Robust Salient Object Detection in Adverse Weather Conditions
- 提出双分支网络，融合天气感知特征与语义信息进行检测
- 在14,945张带天气噪声的图像上实现领先性能
- 适合关注真实场景鲁棒性的视觉算法研究者
复杂环境下显著物体检测（SOD）仍是挑战性课题。现有方法多在自然场景中表现良好，常依赖深度、红外等多模态信息提升精度，但对天气噪声影响缺乏系统研究，主要因缺乏像素级标注数据集。为此，本文提出新的天气扩展显著物体检测数据集WXSOD，包含14,945张含多样天气噪声的RGB图像，配有真实标注和天气标签。为验证算法泛化能力，数据集设有合成测试集（在清晰图像上添加天气噪声）与真实测试集（含真实世界天气噪声）。基于WXSOD，提出高效基线模型WFANet，采用全监督双分支结构：天气预测分支挖掘天气相关深层特征，显著性检测分支融合主干提取的语义特征与天气特征完成检测。在17种SOD方法对比中，WFANet在WXSOD上表现更优。代码与评测结果将公开于https://github.com/C-water/WXSOD。
原文摘要 · Abstract (English)
Salient object detection (SOD) in complex environments remains a challenging research topic. Most existing methods perform well in natural scenes with negligible noise, and tend to leverage multi-modal information (e.g., depth and infrared) to enhance accuracy. However, few studies are concerned with the damage of weather noise on SOD performance due to the lack of dataset with pixel-wise annotations. To bridge this gap, this paper introduces a novel Weather-eXtended Salient Object Detection (WXSOD) dataset. It consists of 14,945 RGB images with diverse weather noise, along with the corresponding ground truth annotations and weather labels. To verify algorithm generalization, WXSOD contains two test sets, i.e., a synthesized test set and a real test set. The former is generated by adding weather noise to clean images, while the latter contains real-world weather noise. Based on WXSOD, we propose an efficient baseline, termed Weather-aware Feature Aggregation Network (WFANet), which adopts a fully supervised two-branch architecture. Specifically, the weather prediction branch mines weather-related deep features, while the saliency detection branch fuses semantic features extracted from the backbone with weather features for SOD. Comprehensive comparisons against 17 SOD methods shows that our WFANet achieves superior performance on WXSOD. The code and benchmark results will be made publicly available at https://github.com/C-water/WXSOD
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