• Optics and Precision Engineering
  • Vol. 32, Issue 8, 1227 (2024)
Liming LIANG, Pengwei LONG*, Yao FENG, and Baohe LU
Author Affiliations
  • School of Electrical Engineering and Automation,Jiangxi University of Science and Technology,Ganzhou341000, China
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    DOI: 10.37188/OPE.20243208.1227 Cite this Article
    Liming LIANG, Pengwei LONG, Yao FENG, Baohe LU. Improving the lightweight VTG-YOLOv7-tiny for steel surface defect detection[J]. Optics and Precision Engineering, 2024, 32(8): 1227 Copy Citation Text show less

    Abstract

    To address the problems of diverse and complex shapes of steel surface defects, detection target missing, and large number of algorithm parameters, a lightweight VTG-YOLOv7-tiny steel defect detection algorithm was proposed. The method first designed VoVGA-FPN network to reduce the loss of information during information transmission and enhance the network feature fusion ability; second, it constructed a triple coordinate attention mechanism to improve the model's feature extraction ability of spatial and channel information; third, it introduceed ghost shuffle convolution to reduce the model parameters and computation while improving the accuracy; fourth, it added a large target detection layer to improve the problem that some defects in the feature map occupy a large proportion, resulting in low detection accuracy. The improved algorithm was verified on the NEU-DET and Severstal steel defect datasets. Compared with the original model, the mAP of the improved algorithm is increased by 5.7% and 8.5%, respectively; the parameters and computation are reduced by 0.61 M and 4.2 G, respectively; the accuracy and recall are increased by 7.1%, 1.8% and 8.9%, 7.0%, respectively. The experimental results show that the improved algorithm better balances the detection accuracy and lightweight, and provides a reference for edge terminal devices.
    Liming LIANG, Pengwei LONG, Yao FENG, Baohe LU. Improving the lightweight VTG-YOLOv7-tiny for steel surface defect detection[J]. Optics and Precision Engineering, 2024, 32(8): 1227
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