Open AccessResearch on Fabric Defect Detection Based on Multi-branch Residual Network
Author(s) -
Runtian Qin,
Li Yu,
Yujie Fan
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 - 10.1088/1742-6596/1907/1/012057
Subject(s) - hourglass , residual , computer science , field (mathematics) , artificial intelligence , convolution (computer science) , sampling (signal processing) , computer vision , pattern recognition (psychology) , algorithm , mathematics , filter (signal processing) , artificial neural network , physics , pure mathematics , astronomy
Aiming at the problem that traditional object detection models have low recognition accuracy for small and medium-sized defects. Based on the original residual module, this paper adds a new convolution branch that dynamically adjusts the size of the receptive field with the number of network layers, and then replaces the residual module in the Hourglass-54 down-sampling stage, and proposes a new backbone network: Hourglass -MRB. The experimental results show that the Corernet-Saccade model using Hourglass-MRB improves the recognition accuracy of small and medium-sized fabric defects by 5.8% and 5.6%. The overall recognition accuracy of the system reaches 81.5%. Theoretically,the speed of fabric defect detection reaches 110 m/min. This article provides more effective support for advancing the internationalization of textile quality assessment.