Dual-stream encoded fusion saliency detection based on RGB and grayscale images

Existing saliency algorithms based on deep learning are not sufficient to extract features of images. And the features are fused only during decoding. As a result, the edge of saliency detection result is not clear and the internal structure display is not uniform. To solve the above problems, this paper proposes a saliency detection method of dual-stream encoding fusion based on RGB and grayscale image. Firstly, an interactive dual-stream encoder is constructed to extract the feature information of gray stream and RGB stream. Secondly, a multi-level fusion strategy is used to obtain more effective multi-scale features. These features are extended and optimized in the decoding stage by linear transformation with hybrid attention. Finally, We propose a hybrid weighted loss function. So that the prediction results of the model can keep a high level accuracy at pixel level and region level. The experimental results of the model proposed to this paper on 6 public datasets illustrate that: The prediction results of the proposed method are clearer about the edge of salient targets and more uniform within salient targets. And has a more lightweight model size.