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MSFF: A Multi-Scale Feature Fusion Network for Surface Defect Detection of Aluminum Profiles

Lianshan SUN, Jingxue WEI, Hanchao DU, Yongbin ZHANG, Lifeng HE

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Summary :

This paper presents an improved YOLOv3 network, named MSFF-YOLOv3, for precisely detecting variable surface defects of aluminum profiles in practice. First, we introduce a larger prediction scale to provide detailed information for small defect detection; second, we design an efficient attention-guided block to extract more features of defects with less overhead; third, we design a bottom-up pyramid and integrate it with the existing feature pyramid network to construct a twin-tower structure to improve the circulation and fusion of features of different layers. In addition, we employ the K-median algorithm for anchor clustering to speed up the network reasoning. Experimental results showed that the mean average precision of the proposed network MSFF-YOLOv3 is higher than all conventional networks for surface defect detection of aluminum profiles. Moreover, the number of frames processed per second for our proposed MSFF-YOLOv3 could meet real-time requirements.

Publication
IEICE TRANSACTIONS on Information Vol.E105-D No.9 pp.1652-1655
Publication Date
2022/09/01
Publicized
2022/05/30
Online ISSN
1745-1361
DOI
10.1587/transinf.2021EDL8088
Type of Manuscript
LETTER
Category
Image Recognition, Computer Vision

Authors

Lianshan SUN
  Shaanxi University of Science and Technology
Jingxue WEI
  Shaanxi University of Science and Technology
Hanchao DU
  Shaanxi University of Science and Technology
Yongbin ZHANG
  Shaanxi University of Science and Technology
Lifeng HE
  Aichi Prefectural University

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