Rail Surface Defect Detection Based on Dual-Path Feature Fusion

被引:0
|
作者
Zhong, Yinfeng [1 ]
Chen, Guorong [1 ]
机构
[1] Chongqing Univ Sci & Technol, Dept Intelligent Technol & Engn, Chongqing 401331, Peoples R China
关键词
defect detection; dual path; attention mechanism; CLASSIFICATION;
D O I
10.3390/electronics13132564
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the rapid development of rail transit, the workload of track maintenance has increased, making the intelligent identification of rail surface defects crucial for improving detection efficiency. To address issues such as low defect detection accuracy, the loss of feature information due to single-path architecture backbones, and insufficient information interaction in existing rail defect detection methods, we propose a rail surface defect detection method based on dual-path feature fusion (DPF). This method initially employs a dual-path structure to separately extract low-level and high-level features. It then utilizes a combination of attention mechanisms and feature fusion techniques to integrate these features. By doing so, it preserves richer information and enhances detection accuracy and robustness. The experimental results demonstrate that the comprehensive performance of the proposed model is superior to mainstream algorithms.
引用
收藏
页数:16
相关论文
共 50 条
  • [1] A scene text detection based on dual-path feature fusion
    Zhao P.
    Xu B.-P.
    Yan S.
    Liu Z.-Y.
    Kongzhi yu Juece/Control and Decision, 2021, 36 (09): : 2179 - 2186
  • [2] STDPNet: a dual-path surface defect detection neural network based on shearlet transform
    An, Dong
    Hu, Ronghua
    Fan, Liting
    Chen, Zhili
    Liu, Zetong
    Zhou, Peng
    VISUAL COMPUTER, 2024, 40 (08): : 5841 - 5856
  • [3] Dual-path feature extraction based hybrid intrusion detection in IoT networks
    Silivery, Arun Kumar
    Rao, Kovvur Ram Mohan
    Solleti, Ramana
    COMPUTERS & ELECTRICAL ENGINEERING, 2025, 122
  • [4] In-field cotton detection algorithm based on the dual-path feature extraction
    Xu, Yang
    Li, Yanan
    Wu, Hao
    Wen, Hongyu
    JOURNAL OF ELECTRONIC IMAGING, 2021, 30 (04)
  • [5] Dual-path segmentation network for automatic fabric defect detection
    Yu, Zhiqi
    Xu, Yang
    Wang, Yuekun
    Sheng, Xiaowei
    Xie, Guosheng
    TEXTILE RESEARCH JOURNAL, 2023, 93 (23-24) : 5224 - 5236
  • [6] Dual-path dehazing network with spatial-frequency feature fusion
    Wang, Li
    Dong, Hang
    Li, Ruyu
    Zhu, Chao
    Tao, Huibin
    Guo, Yu
    Wang, Fei
    PATTERN RECOGNITION, 2024, 151
  • [7] Attention-based dual-path feature fusion network for automatic skin lesion segmentation
    He, Zhenxiang
    Li, Xiaoxia
    Chen, Yuling
    Lv, Nianzu
    Cai, Yong
    BIODATA MINING, 2023, 16 (01)
  • [8] High-Speed Semantic Segmentation Based on Dual-Path Feature Fusion Codec Structure
    Hu X.
    Gong Y.
    Jing L.
    Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, 2022, 34 (12): : 1911 - 1919
  • [9] Attention-based dual-path feature fusion network for automatic skin lesion segmentation
    Zhenxiang He
    Xiaoxia Li
    Yuling Chen
    Nianzu Lv
    Yong Cai
    BioData Mining, 16
  • [10] CSANet: Contour and Semantic Feature Alignment Fusion Network for Rail Surface Defect Detection
    Yang, Jinxin
    Zhou, Wujie
    Wu, Ruiming
    Fang, Meixin
    IEEE SIGNAL PROCESSING LETTERS, 2023, 30 : 972 - 976