基于国产商用器件的星载智能目标检测技术
收稿日期: 2023-04-12
修回日期: 2023-08-11
录用日期: 2023-09-05
网络出版日期: 2023-09-21
基金资助
国家重点实验室基金(6142113200307);中国电子科技集团公司航天信息应用技术重点实验室开放基金(SXX18629T022)
On⁃board intelligent target detection technology based on domestic commercial components
Received date: 2023-04-12
Revised date: 2023-08-11
Accepted date: 2023-09-05
Online published: 2023-09-21
Supported by
National Key Laboratory Foundation of China(6142113200307);Open Fund Project of China Electronics Technology Corporation Aerospace Information Application Technology Key Laboratory(SXX18629T022)
针对星载计算平台体积、功耗受限难题，提出了“FPGA+AI处理器”的硬件架构，并设计了以国产商用器件复旦微JFM7K325T FPGA和华为Ascend310 AI处理器为核心的星载图像处理智能计算平台；针对深度网络算法计算、存储开销大的难题，提出了轻量化遥感图像舰船目标检测算法，在保证检测精度的同时有效减小了计算量和参数量；针对神经网络算法实时部署计算慢的难题，开展量化感知训练、算子融合的计算图优化，以及多线程并行的软件设计，有效提升了实时计算性能。实验结果表明：该系统功耗<15 W，遥感图像舰船目标检测准确率达0.908，1 152×1 152遥感图像处理帧率达63.3 帧/s，4 096×5 120大幅面图像处理帧率达3.17 帧/s。实现了国产化智能目标检测平台深度学习算法实时部署，为实现自主可控的智能化星载前端系统进行了探索研究。
陈立群 , 邹旭 , 张磊 , 朱颖盼 , 王港 , 陈金勇 . 基于国产商用器件的星载智能目标检测技术[J]. 航空学报, 2023 , 44(S2) : 728860 -728860 . DOI: 10.7527/S1000-6893.2023.28860
To address the problems of limited size and power consumption of the on-board computing platform， the hardware architecture of “FPGA+AI processor” is proposed， and the on-board image processing platform centering on the domestic commercial devices Fudan Micro JFM7K325T FPGA and Huawei Ascend310 AI processor is designed. Intelligent computing platform. To overcome the problem of high computational and storage costs for deep network algorithms， a ship target detection algorithm based on the lightweight remote sensing image is proposed， which effectively reduces the amount of computation and parameters while ensuring the detection accuracy. To solve the problem of slow computing in real-time deployment of neural network algorithms， we carry out quantization-aware training， calculation graph optimization of operator fusion， and multi-threaded parallel software design， which effectively improves real-time computing performance. The experimental results show that the power consumption of the system is less than 15 W， the detection accuracy of ships in remote sensing images reaches 0.908， the frame rate of 1 152×1 152 remote sensing image processing reaches 63.3 frames/s， and the frame rate of 4 096×5 120 large-format image processing reaches 3.17 frames/s. This paper realizes the real-time deployment of the deep learning algorithm of the domestically produced intelligent target detection platform， and explores the realization of an autonomous and controllable intelligent spaceborne front-end system.
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