|本期目录/Table of Contents|

[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(自然科学版),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.
点击复制

基于区域卷积网络的行驶车辆检测算法(PDF)
分享到：

《集美大学学报（自然科学版）》[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 Changyu1; ZHENG Jiachun2; HUANG 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 detection; convolutional neural network; joint training
分类号:
-
DOI:
-
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.

参考文献/References:

相似文献/References:

[1]陈德意,张宏怡,刘彩玲,等.基于关键词策略和CNN的中文文本有害信息分类[J].集美大学学报(自然科学版),2020,25(5):392.
　CHEN Deyi,ZHANG Hongyi,LIU Cailing,et al.Classification of Chinese Text Harmful Information Based on Keywords Strategy and Convolutional Neural Network[J].Journal of Jimei University,2020,25(4):392.
[2]张东晓,陈彦翔.一种面向移动端的浅层CNN表情识别[J].集美大学学报(自然科学版),2021,26(2):129.
　ZHANG Dongxiao,CHEN Yanxiang.Mobile-Oriented Facial Expression Recognition Based on Shallow CNN[J].Journal of Jimei University,2021,26(4):129.
[3]宋策,尹勇,王鹏.基于改进YOLOv5的船舶目标检测算法[J].集美大学学报(自然科学版),2023,28(2):136.
　SONG Ce,YIN Yong,WANG Peng.Ship Target Detection Algorithm Based on Improved YOLOv5[J].Journal of Jimei University,2023,28(4):136.
[4]花海波,于洪亮,闫锦,等.基于应力波卷积神经网络的齿轮故障诊断方法[J].集美大学学报(自然科学版),2023,28(4):335.
　HUA Haibo,YU Hongliang,YAN Jin,et al.Fault Diagnosis of Gear Based on Stress Wave Feature and Convolutional Neural Network[J].Journal of Jimei University,2023,28(4):335.
[5]蔡岱立,谢维波.FMSWFormer：基于频率分离和自适应多尺度窗口的视觉Transformer[J].集美大学学报(自然科学版),2023,28(6):568.
　CAI Daili,XIE Weibo.FMSWFormer:Visual Transformer with Frequency Separation and Adaptive Multi-Scale Window Attention[J].Journal of Jimei University,2023,28(4):568.
[6]张敏.MEC-NOMA系统的物理层安全性能评估[J].集美大学学报(自然科学版),2024,29(2):188.
　ZHANG Min.Performance Evaluation on PHY-Security of MEC-NOMA System[J].Journal of Jimei University,2024,29(4):188.

备注/Memo

备注/Memo:
更新日期/Last Update: 2019-08-31