YOLO-MST: Multiscale deep learning method for infrared small target detection based on super-resolution and YOLO

TL;DR

This paper introduces YOLO-MST, a deep learning approach combining super-resolution and multi-scale feature fusion to improve infrared small target detection accuracy and robustness in complex backgrounds.

Contribution

It proposes a novel YOLO-based network with a multi-scale dynamic detection head and super-resolution preprocessing for enhanced infrared small target detection.

Findings

Achieved 96.4% mAP on SIRST dataset

Achieved 99.5% mAP on IRIS dataset

Outperformed existing methods in detection accuracy

Abstract

With the advancement of aerospace technology and the increasing demands of military applications, the development of low false-alarm and high-precision infrared small target detection algorithms has emerged as a key focus of research globally. However, the traditional model-driven method is not robust enough when dealing with features such as noise, target size, and contrast. The existing deep-learning methods have limited ability to extract and fuse key features, and it is difficult to achieve high-precision detection in complex backgrounds and when target features are not obvious. To solve these problems, this paper proposes a deep-learning infrared small target detection method that combines image super-resolution technology with multi-scale observation. First, the input infrared images are preprocessed with super-resolution and multiple data enhancements are performed. Secondly, based on the YOLOv5 model, we proposed a new deep-learning network named YOLO-MST. This network includes replacing the SPPF module with the self-designed MSFA module in the backbone, optimizing the neck, and finally adding a multi-scale dynamic detection head to the prediction head. By dynamically fusing features from different scales, the detection head can better adapt to complex scenes. The mAP@0.5 detection rates of this method on two public datasets, SIRST and IRIS, reached 96.4% and 99.5% respectively, more effectively solving the problems of missed detection, false alarms, and low precision.