Optimized small object detection in low resolution infrared images using super resolution and attention based feature fusion.

ABSTRACT: Infrared (IR) imaging is extensively applied in domains such as object detection, industrial monitoring, medical diagnostics, intelligent transportation due to its robustness in low-light, adverse weather, and complex environments. However, challenges such as low resolution, high noise, limited texture details, and restricted dynamic range hinder the performance of traditional object detection models. To address these limitations, this study proposes an optimized approach for small object detection in low-resolution IR images by integrating super-resolution reconstruction with an enhanced YOLOv8 model. A lightweight super-resolution network, LightweightSRNet, is designed to enhance low-resolution IR images into high-resolution ones, improving feature quality with minimal computational comp

SUBMITTER: Wang W 

PROVIDER: S-EPMC12273969 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature