Multi-scale Fusion and Non-Local Attention Mechanism Based Salient Object Detection

With the development of deep learning, researches in the field of computer vision are attracting more attention. As the pre-processing operation of visual tasks, a salient model may focus on pure architectures. The paper proposes a new multi-scale fusion network to enrich high-level redundant information with the enlarged receptive field. With the guidance of attention mechanism, the framework can capture more effective correlation spatial and channels information. Building a short-connection between high-level and each level features to transmit the contextual features. The model can be used in a variety of complex scenes for end-to- end image detection, with simple structure and strong versatility. Experimental results obtained on multiple common datasets have shown that the proposed model achieved better performance both in the visual effect and the accuracy for small object and multi-target detection.