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Revisiting Shadow Detection: A New Benchmark Dataset for Complex World
Xiaowei Hu, Tianyu Wang, Chi-Wing Fu, Yitong Jiang, Qiong Wang, Pheng-Ann Heng
IEEE TIP · 2021
Research summary
A comprehensive benchmark dataset for shadow detection in complex real-world scenarios.
Abstract
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.
Key insight
Introduces the CUHK-Shadow dataset of 10,500 annotated images covering diverse real-world shadow scenarios, addressing the lack of comprehensive benchmarks for shadow detection in complex scenes.
arXiv version linked; publication venue as listed above
Paper & resources
BibTeX
@article{hu2021revisiting,
title={Revisiting Shadow Detection: A New Benchmark Dataset for Complex World},
author={Hu, Xiaowei and Wang, Tianyu and Fu, Chi-Wing and Jiang, Yitong and Wang, Qiong and Heng, Pheng-Ann},
journal={IEEE Transactions on Image Processing},
volume={30},
pages={1925--1934},
year={2021},
publisher={IEEE}
}