Revisiting Shadow Detection: A New Benchmark Dataset for Complex World

• Published in: IEEE transactions on image processing Vol. 30; pp. 1925 - 1934
• Main Authors: Hu, Xiaowei, Wang, Tianyu, Fu, Chi-Wing, Jiang, Yitong, Wang, Qiong, Heng, Pheng-Ann
• Format: Journal Article
• Language: English
• Published: United States IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: benchmark dataset; Benchmark testing; Benchmarks; Buildings; complex; Complexity; Complexity theory; Datasets; deep neural network; Feature extraction; Roads; Shadow detection; Shadows; Solid modeling; Training
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2021.3049331

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.