In order to address the issue of unsatisfactory performance in self-supervised monocular depth estimation methods when applied to thin structured regions and boundary regions, this paper proposes a method for self-supervised monocular depth estimation based on FastSAM-assisted representation enhancement. Firstly, without the need for extra supervision, FastSAM is presented to supply the depth network with rich semantic information. Secondly, a semantic guidance module (SGM) is proposed to explore the correlation between semantic features and depth features, and to enhance the global feature representation. Furthermore, to enhance the performance of boundary depth estimation, a edge guiding module (EGM) is built to direct the network to focus more on local features. Extensive experiments show that the proposed method outperforms the state-of-the-art methods, especially in depth estimation of thin-structured regions and boundary regions.

Camouflaged object identification is a new visual detection job that has many applications in several fields. Its goal is to identify camouflaged targets that are completely disguised in their environment. To address the problem of current camouflaged object detection algorithms failing to accurately and completely identify the object's structure and boundaries, this paper designs a bio-heuristic framework based on the human visual perception process when observing camouflaged images, and names its Positioning and Refinement Network (PRNet). Res2Net is used to extract the original features of the image and mine the edge cues of the target from multi-level information. A feature enhancement module is specially designed to expand the perceptual field while enriching the global contextual information. Then, the positioning module utilizes the dual-attention mechanism to locate the approximate position of the target from both channel and spatial dimensions. Lastly, the refinement module leverages multi-type information to further improve the target structure and edges by concentrating on target cues in both the foreground and background. Extensive experimental results on three widely used benchmark datasets for camouflage target detection demonstrate that the overall performance of the proposed network significantly outperforms 14 comparative algorithms and performs well in a variety of complex scenarios.