[1]曹长玉,郑佳春,黄一琦.基于区域卷积网络的行驶车辆检测算法[J].集美大学学报(自然版),2019,24(4):315-320.
 CAO Changyu,ZHENG Jiachun,HUANG Yiqi.Research on Running Vehicle Detection Algorithm-Based on Regional Convolution Network[J].Journal of Jimei University,2019,24(4):315-320.
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基于区域卷积网络的行驶车辆检测算法()
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《集美大学学报(自然版)》[ISSN:1007-7405/CN:35-1186/N]

卷:
第24卷
期数:
2019年第4期
页码:
315-320
栏目:
数理科学与信息工程
出版日期:
2019-07-28

文章信息/Info

Title:
Research on Running Vehicle Detection Algorithm-Based on Regional Convolution Network
作者:
曹长玉1郑佳春2黄一琦1
(1.集美大学航海学院,福建 厦门 361021;2.集美大学信息工程学院,福建 厦门 361021)
Author(s):
CAO Changyu1ZHENG Jiachun2HUANG Yiqi1
(1.Navigation College,Jimei University,Xiamen 361021,China;2.Information Engineering College,Jimei University,Xiamen 361021,China )
关键词:
行驶车辆检测卷积神经网络联合训练
Keywords:
running vehicle detectionconvolutional neural networkjoint training
文献标志码:
A
摘要:
为解决多种天气与多种场景下主干道路行驶车辆检测存在的实时性、泛化能力差、漏检、定位不准确等问题,研究了基于TensorFlow深度学习框架的区域卷积神经网络(Faster R-CNN)算法,通过引入VGG16神经网络模型,优化ROI Pooling Layer,并采用联合训练方法,得到改进的算法模型。采用UA_CAR数据集进行模型训练〖BFQ〗,实现行驶中的车辆检测,测试结果与优化前Faster R-CNN比较,MAP提高了7.3个百分点,准确率提高了7.4个百分点,检测用时0.085 s,提高了对多种环境与场景的适应性。
Abstract:
In order to solve the issue of poor real-time performance,poverty generalization ability,missed detection and inaccurate location of running vehicle detection on currently main roads in various weather and various scenarios,the regional convolutional neural network (Faster R-CNN) algorithm based on TensorFlow framework of deep learning is studied.By introducing the VGG16 neural network model,optimizing the ROI Pooling Layer,and adopting method of joint training ,an improved algorithm model is obtained.The UA_CAR database is used for model training to carry out vehicle detection in the course of driving.Compared with the Faster R-CNN before optimization,the test results show that the MAP is increased by 7.3 percentage points,the accuracy rate is increased by 7.4 percentage points,and the detection time is 0.085s.The network improved adaptability to multiple environments and scenarios.
更新日期/Last Update: 2019-08-31