计算机与现代化Issue(1)：25-29,5.DOI:10.3969/j.issn.1006-2475.2025.01.005
基于国产AI芯片的目标检测算法优化与部署
Optimization and Deployment of Object Detection Algorithm Based on Domestic AI Chips
摘要
Abstract
At present,various types of neural networks have gradually been widely applied in all aspects of society.The perfor-mance of neural network models largely depends on the quality of their training strategies,and their deployment cannot be sepa-rated from the support of corresponding hardware platforms.In order to ensure the information security and development of the electronic information industry in China under the current situation,it is urgent to replace relevant domestic AI chips.Taking the replacement of domestic AI chips as the starting point,this article explores the deployment process of neural network algorithms on domestic platforms based on the Quanai QA-200RC development kit.The improvement of YOLOv6 neural network training and host program optimization are carried out according to specific task requirements.With real-time detection through cameras,target detection of rocket debris is achieved,the frame rate is 30 FPS,the mAP_0.5 is 90.1%,and the power consumption is 8.1 W,which meets the requirements for completing object detection tasks on edge platforms and is helpful for promoting the applica-tion of domestic chips in related fields.关键词
目标检测/神经网络/模型训练/AI芯片/国产化/硬件平台/PythonKey words
target detection/neural network/model training/AI chip/localization/hardware platform/Python分类
信息技术与安全科学引用本文复制引用
陈思贇,马怀波,张华君,兰子柠,陈文鑫,胡杰,常胜..基于国产AI芯片的目标检测算法优化与部署[J].计算机与现代化,2025,(1):25-29,5.基金项目
国家自然科学基金面上项目(62074116) （62074116）
武汉市知识创新专项(2023010201010077) （2023010201010077）
武汉市重点研发计划项目(2023010402010597) （2023010402010597）
武汉大学珞珈青年学者基金资助项目 （）
