The defect detection for X-ray images based on a new lightweight semantic segmentation network

Xin Yi; Chen Peng; Zhen Zhang; Liang Xiao; Xin Yi; Chen Peng; Zhen Zhang; Liang Xiao

doi:10.3934/mbe.2022193

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 4: 4178-4195. doi: 10.3934/mbe.2022193

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Research article Special Issues

The defect detection for X-ray images based on a new lightweight semantic segmentation network

School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China

Academic Editor: Hamid Reza Karimi

Received: 05 January 2022 Revised: 05 February 2022 Accepted: 11 February 2022 Published: 17 February 2022

The tire factory mainly inspects tire quality through X-ray images. In this paper, an end-to-end lightweight semantic segmentation network is proposed to realize the error detection of bead toe. In the network, firstly, the texture feature of different regions of tire is extracted by an encoder. Then, we introduce a decoder to fuse the output feature of the encoder. As the dimension of the feature maps is reduced, the positions of bead toe in the X-ray image have been recorded. When evaluating the final segmentation effect, we propose a local mIoU(L-mIoU) index. The segmentation accuracy and reasoning speed of the network are verified on the tire X-ray image set. Specifically, for 512 $ \times $ 512 input images, we achieve 97.1% mIoU and 92.4% L-mIoU. Alternatively, the bead toe coordinates are calculated using only 1.0 s.
- tire X-ray images,
- defect detection,
- image processing,
- lightweight semantic segmentation network,
- auxiliary supervision
Citation: Xin Yi, Chen Peng, Zhen Zhang, Liang Xiao. The defect detection for X-ray images based on a new lightweight semantic segmentation network[J]. Mathematical Biosciences and Engineering, 2022, 19(4): 4178-4195. doi: 10.3934/mbe.2022193

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Abstract

The tire factory mainly inspects tire quality through X-ray images. In this paper, an end-to-end lightweight semantic segmentation network is proposed to realize the error detection of bead toe. In the network, firstly, the texture feature of different regions of tire is extracted by an encoder. Then, we introduce a decoder to fuse the output feature of the encoder. As the dimension of the feature maps is reduced, the positions of bead toe in the X-ray image have been recorded. When evaluating the final segmentation effect, we propose a local mIoU(L-mIoU) index. The segmentation accuracy and reasoning speed of the network are verified on the tire X-ray image set. Specifically, for 512 $ \times $ 512 input images, we achieve 97.1% mIoU and 92.4% L-mIoU. Alternatively, the bead toe coordinates are calculated using only 1.0 s.

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