A novel single robot image shadow detection method based on convolutional block attention module and unsupervised learning network - Zhang, Jun; Liu, Junjun (Frontiers in Neurorobotics, 2022)
Shadow detection plays a very important role in image processing. Although many algorithms have been proposed in different environments, it is still a challenging task to detect shadows in natural scenes. In this paper, we propose an unsupervised domain adaptation adversarial learning network for single image shadow detection. The new method mainly contains three steps. Firstly, in order to reduce the data deviation between the domains, the hierarchical domain adaptation strategy is adopted to calibrate the feature distribution from low level to high level between the source domain and the target domain. Secondly, in order to enhance the soft shadow detection ability of the model, the boundary adversarial branch is proposed to obtain structured shadow boundary. Thirdly, the entropy adversarial branch is combined to further suppress the high uncertainty at the boundary of the prediction results, and it obtains the smooth and accurate shadow boundary. Finally, we conduct abundant experiments on public datasets, the results show that the proposed shadow detection method has better edge structure compared with the existing deep learning detection methods.