Revisiting Shadow Detection: A New Benchmark Dataset for Complex World
This work addresses the challenge of accurate shadow detection for computer vision applications, but it is incremental as it builds on existing methods with a new dataset.
The authors tackled the problem of shadow detection in complex real-world photos by creating a new benchmark dataset of 10,500 images with labeled masks, and they demonstrated the effectiveness of their fast detection network with a detail enhancement module.
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.