Image-Based Pavement Type Classification with Convolutional Neural Networks

被引：0
|
作者：
Riid, Andri [1 ]
Manna, Davide Liberato [1 ]
Astapov, Sergei [1 ]
机构：
[1] Tallinn Univ Technol, Dept Software Sci, Tallinn, Estonia
来源：
2020 IEEE 24TH INTERNATIONAL CONFERENCE ON INTELLIGENT ENGINEERING SYSTEMS (INES 2020) | 2020年
关键词：
road attributes; texture classification; deep learning;
D O I：
10.1109/ines49302.2020.9147199
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Road pavement type classification is important in route planning, road maintenance and for autonomous vehicles. In this paper, we propose a deep learning based method for automatic road type classification from road surface images. The resulting binary classifiers (paved and non-paved road classes) achieve up to 98% classification accuracy on the test set that contains over 100 000 real-world road images that cover a distance of over 300 km.
引用
收藏
页码：55 / 60
页数：6
相关论文
共 50 条
  • [1] Pretraining Convolutional Neural Networks for Image-Based Vehicle Classification
    Han, Yunfei
    Jiang, Tonghai
    Ma, Yupeng
    Xu, Chunxiang
    [J]. ADVANCES IN MULTIMEDIA, 2018, 2018
  • [2] Automatic Pavement Type Recognition for Image-Based Pavement Condition Survey Using Convolutional Neural Network
    Yang, Guangwei
    Wang, Kelvin C. P.
    Li, Joshua Qiang
    Fei, Yue
    Liu, Yang
    Mahboub, Kamyar C.
    Zhang, Allen A.
    [J]. JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 2021, 35 (01)
  • [3] Lagged encoding for image-based time series classification using convolutional neural networks
    Jastrzebska, Agnieszka
    [J]. STATISTICAL ANALYSIS AND DATA MINING, 2020, 13 (03) : 245 - 260
  • [4] Image-based classification of wheat spikes by glume pubescence using convolutional neural networks
    Artemenko, Nikita V.
    Genaev, Mikhail A.
    Epifanov, Rostislav UI.
    Komyshev, Evgeny G.
    Kruchinina, Yulia V.
    Koval, Vasiliy S.
    Goncharov, Nikolay P.
    Afonnikov, Dmitry A.
    [J]. FRONTIERS IN PLANT SCIENCE, 2024, 14
  • [5] Image-based Text Classification using 2D Convolutional Neural Networks
    Merdivan, Erinc
    Vafeiadis, Anastasios
    Kalatzis, Dimitrios
    Hanke, Sten
    Kropf, Johannes
    Votis, Konstantinos
    Giakoumis, Dimitrios
    Tzovaras, Dimitrios
    Chen, Liming
    Hamzaoui, Raouf
    Geist, Matthieu
    [J]. 2019 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTING, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI 2019), 2019, : 144 - 149
  • [6] Image-Based Macroscopic Classification of Aspergillus Fungi Species Using Convolutional Neural Networks
    Billones, Robert Kerwin C.
    Calilung, Edwin J.
    Dadios, Elmer P.
    Santiago, Nelson
    [J]. 2020 IEEE 12TH INTERNATIONAL CONFERENCE ON HUMANOID, NANOTECHNOLOGY, INFORMATION TECHNOLOGY, COMMUNICATION AND CONTROL, ENVIRONMENT, AND MANAGEMENT (HNICEM), 2020,
  • [7] Image-Based Malware Classification Using Convolutional Neural Network
    Kim, Hae-Jung
    [J]. ADVANCES IN COMPUTER SCIENCE AND UBIQUITOUS COMPUTING, 2018, 474 : 1352 - 1357
  • [8] Image-based malware representation approach with EfficientNet convolutional neural networks for effective malware classification
    Chaganti, Rajasekhar
    Ravi, Vinayakumar
    Pham, Tuan D.
    [J]. JOURNAL OF INFORMATION SECURITY AND APPLICATIONS, 2022, 69
  • [9] Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification
    Paardekooper, Cornelius
    Noman, Nasimul
    Chiong, Raymond
    Varadharajan, Vijay
    [J]. 2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2022,
  • [10] Image-based microstructure classification of mortar and paste using convolutional neural networks and transfer learning
    Qian, Hanjie
    Li, Ye
    Yang, Jianfei
    Xie, Lihua
    Tan, Kang Hai
    [J]. CEMENT & CONCRETE COMPOSITES, 2022, 129
12345