Papers › Revisiting Shadow Detection: A New Benchmark Dataset for Complex World
Revisiting Shadow Detection: A New Benchmark Dataset for Complex World
Xiaowei Hu, Tianyu Wang, Chi-Wing Fu, Yitong Jiang, Qiong Wang, Pheng-Ann Heng
Shadow detection in general photos is a nontrivial problem, due to the complexity of the real world. Though recent shadow detectors have already achieved remarkable performance on various benchmark data, their performance is still limited for general real-world situations. In this work, we collected shadow images for multiple scenarios and compiled a new dataset of 10,500 shadow images, each with labeled ground-truth mask, for supporting shadow detection in the complex world. Our dataset covers a rich variety of scene categories, with diverse shadow sizes, locations, contrasts, and types. Further, we comprehensively analyze the complexity of the dataset, present a fast shadow detection network with a detail enhancement module to harvest shadow details, and demonstrate the effectiveness of our method to detect shadows in general situations.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Shadow Detection | CUHK-Shadow | FSDNet (TIP 2021) (512x512) | BER | 8.84 | #10 of 16 | Archive leaderboard | report |
| Shadow Detection | CUHK-Shadow | FSDNet (TIP 2021) (256x256) | BER | 9.93 | #13 of 16 | Archive leaderboard | report |
| Shadow Detection | SBU / SBU-Refine | FSDNet (TIP 2021) (512x512) | BER | 6.8 | #13 of 16 | Archive leaderboard | report |
| Shadow Detection | SBU / SBU-Refine | FSDNet (TIP 2021) (256x256) | BER | 7.16 | #15 of 16 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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