现代电子技术2024，Vol.47Issue(7)：140-144,5.DOI:10.16652/j.issn.1004-373x.2024.07.024
基于深度卷积神经网络的汽车图像分类算法与加速研究
Research on automobile image classification algorithm and acceleration based on deep convolutional neural network
黄佳美 1张伟彬 1熊官送1
作者信息
- 1. 北京自动化控制设备研究所,北京 100074
- 折叠
摘要
Abstract
In view of the prominent contradiction between the large demand for computing power of the image object classification algorithm model based on deep convolutional neural network(DCNN)and the limited resources after the deployment of edge devices,it is of great significance to design the acceleration unit of edge computing equipment to ensure the accuracy and real-time performance of the classification algorithm in the field of edge computing and recognition of illegal occupation of bus lanes and illegal vehicles.Focused on the above,a bus classification algorithm based on DCNN,which realizes the acceleration of bus image classification algorithm on FPGA,is proposed.The ResNet50 pre-trained model is fine-tuned based on the transfer learning method.The inference acceleration on the embedded side is used to realize the inference of the model,and the inference deployment implementation of the FPGA acceleration scheme is implemented.The results show that the proposed algorithm has flexible hardware configuration and fast information processing acceleration,which provides an effective solution for the efficient and high-speed application of neural networks in embedded platforms.关键词
图像分类/边缘计算/卷积神经网络/迁移学习/ResNet50模型/加速推理Key words
image classification/edge computing/CNN/transfer learning/ResNet50 model/inference acceleration分类
信息技术与安全科学引用本文复制引用
黄佳美,张伟彬,熊官送..基于深度卷积神经网络的汽车图像分类算法与加速研究[J].现代电子技术,2024,47(7):140-144,5.
