Open AccessMulti-operator feature enhancement methods for industrial defect detection
Author(s) -
Bo Zhou,
Yi Chao Fan,
Yuxin Liu,
XuDong Yin
Publication year - 2021
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 - 10.1088/1742-6596/2078/1/012030
Subject(s) - preprocessor , operator (biology) , computer science , artificial intelligence , edge detection , data pre processing , feature (linguistics) , computer vision , scheme (mathematics) , object detection , pattern recognition (psychology) , feature extraction , field (mathematics) , enhanced data rates for gsm evolution , image processing , image (mathematics) , mathematics , mathematical analysis , biochemistry , chemistry , linguistics , philosophy , repressor , transcription factor , pure mathematics , gene
Deep learning based object detection algorithms have been gradually applied to industrial defect detection, but the resulted accuracy does not fully meet the needs of industrial inspection. In order to enhance image features, this paper proposes a series of image preprocessing schemes based on edge detection operators, using a single-operator preprocessing scheme, a multi-operator serial preprocessing scheme and a multi-operator parallel preprocessing scheme for image preprocessing of data to enhance the edge features of images. The validation experiment of the SSD based object detection algorithm is performed on dataset used for industrial inspection, to verify the effectiveness of the processing schemes above. The result shows that the multi-operator based image preprocessing method is effective in improving the accuracy of surface defect detection in the field of industrial defect detection.