Detection of space infrared weak target based on YOLOv4

Yang-fan LIU; Li-hua CAO; Ning LI; Yun-feng ZHANG

doi:10.37188/CJLCD.2020-0227

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Detection of space infrared weak target based on YOLOv4

Image Processing | 更新时间：2021-04-02

- Detection of space infrared weak target based on YOLOv4
- Chinese Journal of Liquid Crystals and Displays Vol. 36, Issue 4, Pages: 615-623(2021)
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
  2.中国科学院大学, 北京 100049
  3.激光与物质相互作用国家重点实验室, 吉林长春 130033
- 作者简介：
- 基金信息：
  
  Supported by National Natural Science Foundation of China(61705219);Jilin Province Science and Technology Development Plan Project(20190103133JH)
- DOI：10.37188/CJLCD.2020-0227
  CLC： TP391.4
- Received：07 September 2020，
  
  Revised：29 October 2020，
  
  Published：2021-04
- 稿件说明：
移动端阅览
Yang-fan LIU, Li-hua CAO, Ning LI, et al. Detection of space infrared weak target based on YOLOv4[J]. Chinese journal of liquid crystals and displays, 2021, 36(4): 615-623.
DOI：

Yang-fan LIU, Li-hua CAO, Ning LI, et al. Detection of space infrared weak target based on YOLOv4[J]. Chinese journal of liquid crystals and displays, 2021, 36(4): 615-623. DOI： 10.37188/CJLCD.2020-0227.

摘要

在空间红外弱目标检测问题中，相关模板法和帧间差分法等传统算法判别率较低，且对数据质量要求较高。针对这一问题，本文提出了一种基于改进YOLOV4的空间红外弱目标检测方法，该算法首先针对空间不同红外目标建立了相应的数据集；以YOLOv4为基础建立了空间目标检测任务专用的神经网络框架，利用k-means聚类算法重新构造先验框；针对红外弱目标的特性设计了多尺度融合算法来提高弱目标的检测精度；最后应用COCO数据集和实验室采集到的红外图像数据集对本算法进行了训练和测试。试验结果表明，本文改进算法较YOLOv4算法在检测的准确性上有明显提升，其平均准确率（AP）可达93.25%以上，检测速度达到了38.99 ms/frame，验证了算法对于空间红外弱目标检测的有效性，很好地满足了空间红外弱目标检测任务的需求。

Abstract

In the problem of space infrared weak target detection

traditional algorithms such as correlation template method and center of gravity

etc.

have a low discrimination rate and high data quality requirements. To solve this problem

the space infrared weak target detection algorithm based on improved YOLOv4 is proposed in this paper. The algorithm first establishes corresponding data sets for different infrared targets in space. Based on YOLOv4

a special neural network framework for space target detection tasks is established. The k-means clustering algorithm is used to reconstruct the prior frame

and multi-scale fusion is designed according to the characteristics of infrared weak targets to improve the detection accuracy of weak targets. Finally

COCO data set and the infrared image data set collected in the laboratory are used to train the algorithm and test. The test results show that the improved algorithm has a significant improvement in the accuracy of detection compared with the YOLOv4 algorithm. Its average accuracy (AP) can reach more than 93.25%

and the detection speed has reached 38.99 ms/frame

which verifies that the effectiveness of target detection satisfies the needs of space infrared weak target detection tasks.

关键词

Keywords

references

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