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计算机系统应用:2020,29(3):93-99
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基于自动驾驶系统的轻量型卷积神经网络优化
(广西科技大学 机械与交通工程学院, 柳州 545000)
Optimization of Lightweight Convolution Neural Network Based on Automatic Driving System
(School of Mechanical and Transportation Engineering, Guangxi University of Science and Technology, Liuzhou 545000 china)
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投稿时间:2019-08-01    修订日期:2019-09-02
中文摘要: 计算机视觉技术大量应用于自动驾驶系统,主要解决物体识别与物体分类问题,本文根据任务提出了一种轻量化的神经网络结构.为解决训练数据规模不足的问题,采用了改进型数据增强算法,使训练数据成倍增加.同时为解决使用数据生成器作为验证集,无法使用tensorboard的问题,提出了解决方案,通过卷积网络可视化方法详细研究了神经网络处理图像信息的原理并提出了优化方法.训练后的模型在验证集上准确率达到了97.5%,满足了自动驾驶系统对分类任务准确率的要求.
Abstract:Computer vision technology is widely used in autopilot system, which mainly solves the problem of object recognition and object classification. In this study, a lightweight neural network structure is proposed according to the task. In order to solve the problem of insufficient training data, an improved data enhancement algorithm is adopted to double the training data. At the same time, in order to solve the problem of using data generator as verification set and unable to use tensorboard, a solution is proposed. The principle of neural network processing image information is studied in detail by convolution network visualization method, and the optimization method is put forward. The accuracy of the trained model is 97.5% on the verification set, which meets the accuracy needs of autopilot system for classification task.
文章编号:7320     中图分类号:    文献标志码:
基金项目:国家自然科学基金(51765007);广西自然科学基金(2016GXNSFAA380111)
引用文本:
高秀龙,葛动元.基于自动驾驶系统的轻量型卷积神经网络优化.计算机系统应用,2020,29(3):93-99
GAO Xiu-Long,GE Dong-Yuan.Optimization of Lightweight Convolution Neural Network Based on Automatic Driving System.COMPUTER SYSTEMS APPLICATIONS,2020,29(3):93-99

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