Scholarlink

<p>In recent years, the technology for detecting small and dim infrared targets has played a crucial role in both military and civilian security fields. Deep learning-based methods have also achieved remarkable progress in this area. However, it is still restricted by challenges such as small target size, low signal-to-noise ratio, and complex backgrounds. Therefore, this paper proposes an improved model IRE-YOLO based on You Only Look Once (YOLO) to enhance the detection accuracy of small targets. To improve the model’s feature extraction ability for targets, a receptive field enhancement module based on dilated convolution and shared weights is proposed. By expanding the receptive field of the feature map, it can extract the detailed features and local information of multi-scale targets. Secondly, to address the difficulties of small target size and low image resolution, a space-to-depth convolution is added to the backbone network. By converting the spatial dimension into the depth dimension, it can effectively capture the context information of small targets. In addition, to enhance the accuracy of the model for real detection boxes, this paper proposes an SNS algorithm, which can effectively remove redundant detection boxes. IRE-YOLO is compared and evaluated with other models on two public datasets, IDTA and SIRST. The experimental results show that compared with the baseline YOLOv5s, the mean average precision (mAP) of IRE-YOLO has increased by 2% and 2.1%, respectively, significantly improving the detection accuracy of small and dim infrared targets.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

IRE-YOLO: Infrared weak target detection algorithm based on the fusion of multi-scale receptive fields and efficient convolution

  • 期刊论文

  • 发表日期：

    2025年2月28日

查看全文
  • Qingxiao Ma,
  • Xiangsuo Fan,
  • Huajin Chen,
  • Tingting Li

摘要

In recent years, the technology for detecting small and dim infrared targets has played a crucial role in both military and civilian security fields. Deep learning-based methods have also achieved remarkable progress in this area. However, it is still restricted by challenges such as small target size, low signal-to-noise ratio, and complex backgrounds. Therefore, this paper proposes an improved model IRE-YOLO based on You Only Look Once (YOLO) to enhance the detection accuracy of small targets. To improve the model’s feature extraction ability for targets, a receptive field enhancement module based on dilated convolution and shared weights is proposed. By expanding the receptive field of the feature map, it can extract the detailed features and local information of multi-scale targets. Secondly, to address the difficulties of small target size and low image resolution, a space-to-depth convolution is added to the backbone network. By converting the spatial dimension into the depth dimension, it can effectively capture the context information of small targets. In addition, to enhance the accuracy of the model for real detection boxes, this paper proposes an SNS algorithm, which can effectively remove redundant detection boxes. IRE-YOLO is compared and evaluated with other models on two public datasets, IDTA and SIRST. The experimental results show that compared with the baseline YOLOv5s, the mean average precision (mAP) of IRE-YOLO has increased by 2% and 2.1%, respectively, significantly improving the detection accuracy of small and dim infrared targets.