Enhanced object detection in low-visibility haze conditions with YOLOv9s.

ABSTRACT: Low-visibility haze environments, marked by their inherent low contrast and high brightness, present a formidable challenge to the precision and robustness of conventional object detection algorithms. This paper introduces an enhanced object detection framework for YOLOv9s tailored for low-visibility haze conditions, capitalizing on the merits of contrastive learning for optimizing local feature details, as well as the benefits of multiscale attention mechanisms and dynamic focusing mechanisms for achieving real-time global quality optimization. Specifically, the framework incorporates Patchwise Contrastive Learning to fortify the correlation among positive samples within image patches, effectively reducing negative sample interference and enhancing the model's capability to discern subtle

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC11849987 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature