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LIU Bing, ZHOU Hai, BIAN Chunjiang, CHENG Xiaolei, WANG Pengfei, ZHANG Biao. Lightweight Yolov5 Algorithm Target Detection System Based on Space-grade NPU (in Chinese). Chinese Journal of Space Science, 2025, 45(4): 1-11 doi: 10.11728/cjss2025.04.2024-0103
Citation: LIU Bing, ZHOU Hai, BIAN Chunjiang, CHENG Xiaolei, WANG Pengfei, ZHANG Biao. Lightweight Yolov5 Algorithm Target Detection System Based on Space-grade NPU (in Chinese). Chinese Journal of Space Science, 2025, 45(4): 1-11 doi: 10.11728/cjss2025.04.2024-0103
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Lightweight Yolov5 Algorithm Target Detection System Based on Space-grade NPU

doi: 10.11728/cjss2025.04.2024-0103 cstr: 32142.14.cjss.2024-0103
  • 1.

    National Space Science Center, Chinese Academy of Sciences, Beijing 100190

  • 2.

    University of Academy of Sciences, Beijing 100049

  • Received Date: 2024-08-20
  • Accepted Date: 2025-07-10
  • Rev Recd Date: 2024-12-25
    • Available Online: 2024-12-31
    Abstract
  • With the continuous progress and expansion of space exploration missions, the quantity of remote sensing images that need to be processed has been increased substantially. In such a context, the target detection systems are confronted with ever-higher demands in terms of robustness and timeliness. The traditional approach of transmitting a large volume of remote sensing data back to the ground for processing has become infeasible due to various limitations such as communication bandwidth and time delay. To address this critical issue, this research focuses on the on-orbit target detection system of remote sensing images, which is based on the astronautics-grade Neural Network Processor (NPU). Specifically, the Yolov5s network is taken as the foundation and optimized. The components with relatively low compatibility with the NPU are replaced, and an attention mechanism is incorporated. This not only overcomes the challenges that the complex network structure and excessive computational requirements of deep learning-based object detection algorithms pose for deployment on satellite processing platforms with limited resources, but also compensates for potential losses in network accuracy. The optimized network is trained iteratively on the GPU using the public dataset VOC. After the CPU-NPU parallel co-processing design, the three main aspects of image processing, namely image preprocessing, feature extraction, and target classification and localization, are executed in parallel. This approach maximizes the utilization of the limited computing and storage resources of the Yulong810A platform. Experimental results demonstrate that when the optimized network is deployed on the Yulong810A on-board processing platform, it achieves remarkable improvements. The number of parameters is significantly reduced by 75%, and compared with the original Yolov5s network, the accuracy is enhanced. The mean Average Precision (mAP) value reaches 71.25%, and the target detection speed attains 47.67 frames per second (fps), which is more than twice the speed of the original Yolov5s network. In summary, this research realizes a more lightweight and faster object detection system, which holds great potential for promoting the development and efficiency of space exploration missions.

     

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