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

TL;DR

This paper introduces WXSOD, a new dataset for salient object detection under adverse weather conditions, and proposes WFANet, a model that effectively handles weather noise, improving detection accuracy in challenging environments.

Contribution

The paper presents the first large-scale dataset with weather noise annotations and a novel weather-aware network for robust salient object detection.

Findings

WFANet outperforms 17 existing SOD methods on WXSOD.

WXSOD includes synthesized and real weather noise test sets.

The dataset enables better evaluation of SOD in adverse weather.

Abstract

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