Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (2): 169-175.DOI: 10.3778/j.issn.1002-8331.2007-0509

• Pattern Recognition and Artificial Intelligence • Previous Articles     Next Articles

Ground Nephogram Object Detection Algorithm Based on Improved Loss Function

WANG Shengchun, CHEN Yang   

  1. College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China
  • Online:2022-01-15 Published:2022-01-18



  1. 湖南师范大学 信息科学与工程学院,长沙 410081

Abstract: Cloud is a common weather phenomenon, and cloud shape is a key feature of weather prediction. At present, cloud observations in ground nephogram images mainly rely on the visual observations of meteorological observers, and rely heavily on the experience of the observers, with low real-time performance and low efficiency. In response to this problem, the application of deep learning is proposed for ground nephogram detection and recognition. Firstly, a new object detection bounding box loss function UIoU is designed and applied to the YOLOv3 algorithm. And the [K]-means clustering algorithm is used to redesign the anchor box size suitable for ground nephogram datasets, so that the bounding box returns more precisely and stably. Experimental results show that the accuracy of UIoU-YOLOv3 has been effectively improved in comparision with original algorithm, and the mAP values on the VOC datasets and the ground nephogram datasets have been increased by 3.4 percentage points and 2.56 percentage points, respectively.

Key words: object detection, ground nephogram recognition, YOLOv3, distance IoU loss(DIoU)

摘要: 云是一种常见的天气现象,云状是天气预测的关键特征。目前,地基云图像中的云状观测主要依赖于气象观测员的目视观测,十分依赖观测员的经验,实时性和效率较低。针对这一问题,提出使用深度学习的方法进行地基云状检测识别。设计了一种新的目标检测边界框损失函数UIoU,将其应用于YOLOv3算法上。并且使用了[K]-means聚类算法重新设计了适用于地基云状数据集的先验框尺寸,使得边界框回归更加精确和稳定。实验结果表明UIoU-YOLOv3相比于原算法精度得到了有效提升,在VOC数据集和地基云状数据集上mAP数值分别提升了3.4个百分点和2.56个百分点。

关键词: 目标检测, 地基云状识别, YOLOv3, DIoU