Texture and semantic integrated small objects detection in foggy scenes.

ABSTRACT: In recent years, small objects detection has received extensive attention from scholars for its important value in application. Some effective methods for small objects detection have been proposed. However, the data collected in real scenes are often foggy images, so the models trained with these methods are difficult to extract discriminative object features from such images. In addition, the existing small objects detection algorithms ignore the texture information and high-level semantic information of tiny objects, which limits the improvement of detection performance. Aiming at the above problems, this paper proposes a texture and semantic integrated small objects detection in foggy scenes. The algorithm focuses on extracting discriminative features unaffected by the environment, and

SUBMITTER: Fang Z 

PROVIDER: S-EPMC9387851 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature