Computer and Modernization ›› 2022, Vol. 0 ›› Issue (12): 74-80.

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Composite Object Detection Based on Improved YOLOv3 from High-resolution Remote Sensing Image


  1. (1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China;
    2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences,
    Beijing 100049, China; 3. Key Laboratory of Spatial Information Processing and Application
    System Technology, Chinese Academy of Sciences, Beijing 100190, China)
  • Online:2023-01-04 Published:2023-01-04

Abstract: Compared with a single object, the composite object of remote sensing image has multiple structures, and there are certain differences between the structures. The composite object has the characteristics of variability and complexity; the remote sensing image is wide and the background is complex, and there are many areas similar to the characteristics of the composite object to be inspected. The above two points lead to the low accuracy of the composite object detection. In response to this problem, this article develops research on composite object detection based on high-resolution remote sensing images. This paper first carries out object characteristic analysis and sample data labeling; then proposes an improved YOLOv3 detection algorithm based on Coordinate Attention attention mechanism and Focal Loss function; finally, an experiment is carried out with a composite target of a basketball court as an example. The experimental results show that compared with the original YOLOv3 algorithm, the recall rate and average detection accuracy of the improved algorithm are increased by 10.3 percentage points and 28.8 percentage points, respectively. The result verifies the feasibility and rationality of the proposed scheme.

Key words: object detection, attention mechanism, loss function, composite object