Open AccessInfrared Tiny Structureless Object Detection Enhanced By Video Super-resolution
Author(s) -
Duixu Mao,
Xiaxu Chen,
Linhan Xu,
Jun Ke
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3571965
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The precise detection of infrared (IR) tiny objects against complex backgrounds is of great significance in the field of aircraft imaging guidance. However, due to the extremely small target features, low brightness, and the tendency to be submerged in complex backgrounds, the detection of IR tiny aerial targets remains a challenge. Concurrently, super-resolution technology is a novel technique that has emerged in recent years, aiming to enhance image quality without altering hardware specifications, thereby magnifying the characteristics of tiny targets. In light of this, we introduce infrared tiny object detection method enhanced by video super resolution. Our proposed method is a two-stage model. Initially, it enhances target features through bidirectional propagation and optical flow alignment within the super-resolution module. Subsequently, it employs restricted receptive field convolution and multi-receptive field feature fusion to enhance the detection accuracy of small targets. Experimental results demonstrate that our method has achieved state-of-the-art performance in the task of IR tiny target detection, with an F1 score of 0.957, an accuracy of 99.2%, and a recall rate of 92.4% on the SIATD dataset, outperforming the state-of-the-art by 2%. Furthermore, ablation studies confirm the significant contribution of super-resolution techniques in enhancing the performance of infrared tiny target detection.