Shadow Detection in Single RGB Images Using a Context Preserver Convolutional Neural Network Trained by Multiple Adversarial Examples

Automatic identification of shadow regions in an image is a basic and yet very important task in many computer vision applications, such as object detection, target tracking, and visual data analysis. Although shadow detection is a well-studied topic, current methods for identification of shadow are not as accurate as required. In this paper, we propose a deep-learning method for shadow detection at a pixel-level that is suitable for single RGB images. The proposed CNN-based method benefits from a novel architecture through which global and local shadow attributes are identified using a new and efficient mapping scheme in the skip connection. It extracts and preserves shadow context in multiple layers and utilizes them gradually in multiple blocks to generate final shadow masks. The training phase of the network is simple and can be directly and easily adapted for other image segmentation tasks. The performance of the proposed system is evaluated on three publicly available datasets, where it outperforms the state-of-the-art balanced error rates (BER) by 3%, 6.2%, and 11.4%.