A self-correction algorithm for transparent object shadow detection

Abstract

Shadow detection for transparent objects is a challenging task. The difficulty arises from the fact that transparent objects and shadow regions are prone to occlusion, and the boundaries of transparent objects become more blurred due to optical effects, ultimately leading to incomplete shadow detection results. In this paper, a novel semisupervised shadow detection algorithm based on self-correction is proposed to address these problems. We construct a shadow detection module based on a hybrid attention mechanism CBAM and integrate the short-term memory capability of LSTM networks, assisting the model in accurately localizing shadow areas based on prior experience. To address the issue of easily overlooked shadow areas, we aim to minimize the difference between the predicted shadow mask and the real shadow mask as our optimization objective. We train the shadow self-correction module using binary cross-entropy loss to enhance the model’s ability to detect shadow areas that are prone to be overlooked. Furthermore, a pretrained boundary detector is utilized to obtain the boundary information between the predicted and real shadow masks. The shadow detection model is then optimized under the constraint of boundary consistency, enabling the model to more accurately identify the boundaries of shadow regions and enhancing the shadow detection performance. The experimental results indicate that, compared to existing shadow detection algorithms, the proposed algorithm performs well in terms of both transparent and nontransparent object shadow detection.