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WXSOD: A Benchmark for Robust Salient Object Detection in Adverse Weather Conditions

arXiv — cs.CV•Wednesday, November 5, 2025 at 5:00:00 AM
PositiveArtificial Intelligence

WXSOD: A Benchmark for Robust Salient Object Detection in Adverse Weather Conditions

The paper presents WXSOD, a newly developed benchmark designed to improve salient object detection (SOD) under adverse weather conditions. Existing SOD methods often face challenges due to noise and the absence of comprehensive datasets tailored for difficult environments. WXSOD addresses these limitations by providing pixel-wise annotations, which aim to enhance the precision of detecting salient objects in such settings. The benchmark's focus on adverse weather scenarios highlights the need for robust detection techniques that can operate reliably despite environmental noise. By introducing this dataset, the research seeks to advance the accuracy and robustness of SOD models when confronted with challenging weather-related visual disturbances. This contribution is positioned as a response to gaps identified in current methodologies and datasets. Overall, WXSOD offers a valuable resource for the computer vision community to develop and evaluate more resilient SOD algorithms.

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