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 complexity. To handle complex backgrounds and scale variations, a Hybrid Global Multi-Head Attention (HG-MHA) mechanism is introduced, enhancing target focus and suppressing noise. An improved SC-BiFPN module is developed to integrate cross-layer feature interactions, boosting small object detection by fusing low-level and high-level features. Additionally, a lightweight C2f-Ghost-Sobel module is designed for efficient edge and detail extraction with reduced computational cost, ensuring real-time detection capabilities. Experimental results on the HIT-UAV dataset show significant performance improvements, with Recall rising from 70.23% to 80.51% and mAP from 77.48% to 83.32%, along with robust performance on other datasets, demonstrating the model’s effectiveness for real-world IR applications. The source code and datasets used in this study are available at: https://github.com/RuopengZhang/infrared-detection-code.