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

• URL: http://arxiv.org/abs/2412.19878v1
• Date: Fri, 27 Dec 2024 18:43:56 GMT
• Title: YOLO-MST: Multiscale deep learning method for infrared small target detection based on super-resolution and YOLO
• Authors: Taoran Yue, Xiaojin Lu, Jiaxi Cai, Yuanping Chen, Shibing Chu,
• Abstract summary: This paper proposes a deep-learning infrared small target detection method that combines image super-resolution technology with multi-scale observation. The mAP@0.5 detection rates of this method on two public datasets, SIRST and IRIS, reached 96.4% and 99.5% respectively.
• Score: 0.18641315013048293
• License:
• 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.

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