Open AccessGlass Defect Detection via Multi-Scale Feature Fusion
Author(s) -
Haiying Huang,
Qiugang Zhan,
Xiurui Xie,
Dongsheng Ye,
Guisong Liu
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2216/1/012099
Subject(s) - detector , computer science , feature (linguistics) , feature extraction , fusion , artificial intelligence , enhanced data rates for gsm evolution , pattern recognition (psychology) , scale (ratio) , physics , telecommunications , philosophy , linguistics , quantum mechanics
Glass defect detection is significant in glass industry. However, most of the existing methods for glass defect detection currently still rely on manual screening with high-cost and poor-efficiency. To address this issue, we propose a glass defect detection method using multi-scale feature fusion strategy. Specifically, we first propose an algorithm based on pix2pix to realize the edge extraction. Then, we propose a glass defect detector based on both local and global features. Comprehensive experiments are conducted on collected glass dataset from factories. The experimental results demonstrate that our method outperforms conventional methods including the traditional image processing and Holistically-Nested Edge Detection (HED), with the precision rate (PR) up to 97%, the false precision rate (FP) below 2% and the total accuracy rate (ACC) of glass defect detection up to 98%.