Optimization and Deployment of Object Detection Algorithm Based on Domestic AI Chips

doi:10.3969/j.issn.1006-2475.2025.01.005

Abstract

Abstract: At present， various types of neural networks have gradually been widely applied in all aspects of society. The performance of neural network models largely depends on the quality of their training strategies， and their deployment cannot be separated 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 application of domestic chips in related fields.

Key words: target detection, neural network, model training, AI chip, localization, hardware platform, Python

CLC Number:

TP391

CHEN Siyun1, MA Huaibo2, ZHANG Huajun2, LAN Zining2, CHEN Wenxin2 , HU Jie1, CHANG Sheng1. Optimization and Deployment of Object Detection Algorithm Based on Domestic AI Chips[J]. Computer and Modernization, 2025, 0(01): 25-29.

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