空间科学学报2025，Vol.45Issue(4)：1123-1133,11.DOI:10.11728/cjss2025.04.2024-0103
基于宇航级NPU的轻量化Yolov5算法的目标检测系统
Lightweight Yolov5 Algorithm Target Detection System Based on Space-grade NPU
摘要
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 re-mote sensing images,which is based on the astronautics-grade Neural Network Processor(NPU).Specifi-cally,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 net-work 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.Experimen-tal results demonstrate that when the optimized network is deployed on the Yulong810A on-board pro-cessing platform,it achieves remarkable improvements.The number of parameters is significantly re-duced 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 re-search realizes a more lightweight and faster object detection system,which holds great potential for pro-moting the development and efficiency of space exploration missions.关键词
空间探测/遥感图像/星上处理平台/深度学习/并行处理设计Key words
Space exploration/Remote sensing images/On-board processing platform/Deep learning/Parallel processing design分类
航空航天引用本文复制引用
刘冰,周海,卞春江,成晓蕾,王鹏飞,张彪..基于宇航级NPU的轻量化Yolov5算法的目标检测系统[J].空间科学学报,2025,45(4):1123-1133,11.基金项目
中国科学院青年创新促进会项目资助(E0293401) （E0293401）
