Review of salient object detection: methods, challenges and directions

Salient object detection, as a key research direction in the field of computer vision, is also one of the hotspots of academic research. In this paper, we systematically sort out the research methods, challenges and future development directions in this field. First, the development of salient object detection is summarized, as well as its wide application in the field of computer vision. Second, a detailed review of salient object detection methods is provided, covering detection methods based on saliency features as well as those under the deep learning framework. Third, salient object detection methods based on traditional convolutional neural networks and full convolutional neural networks, as well as salient object detection methods based on the attention mechanism, are discussed in depth, and commonly used datasets and evaluation metrics in the field of salient object detection are introduced. Again, the article summarizes and analyzes the current challenges of salient object detection, such as the limitations of existing datasets and the detection accuracy in complex scenes. Finally, it looks forward to the future development direction of salient object detection. Through this review, this article aims to provide a comprehensive and in-depth reference for researchers engaged in salient object detection in order to promote the further development of this field.