Contextual Boundary Aware Network for Salient Object Detection
Jiahao Zheng, Qiaoning Yang · 2024
Currently, for the task of salient object detection (SOD) based on deep learning, most approaches use a strategy of multi-level feature aggregation to enhance performance. However, due to the insufficient utilization of inter-pixel information, the aggregation of multi-level features often affects the prediction of salient objects and results in detecting blurry boundaries of salient objects. To tackle this problem, we have proposed a salient object detection network based on context-aware boundary perception. This network utilizes the context awareness (CA) branch to extract comprehensive contextual semantic information, guiding the network to focus attention not only on salient objects, but also on learning the mutual relationships between multiple salient objects. In addition, the boundary awareness (BA) branch is utilized to explore detailed boundary information around the contours of salient objects, enhancing the network's understanding of the edge pixels of salient objects. Moreover, we have introduced a new feature interaction aggregation (FIA) module, which is used to merge contextual semantic information and boundary detail information in the decoding stage to effectively utilize multi-level features and generate clearer and more accurate saliency maps. By conducting comprehensive experiments on three public datasets, we have demonstrated that our proposed method outperforms the current state-of-the-art representative methods.