Reference-based Defect Detection Network
- URL: http://arxiv.org/abs/2108.04456v1
- Date: Tue, 10 Aug 2021 05:44:23 GMT
- Title: Reference-based Defect Detection Network
- Authors: Zhaoyang Zeng, Bei Liu, Jianlong Fu, Hongyang Chao
- Abstract summary: The first issue is the texture shift which means a trained defect detector model will be easily affected by unseen texture.
The second issue is partial visual confusion which indicates that a partial defect box is visually similar with a complete box.
We propose a Reference-based Defect Detection Network (RDDN) to tackle these two problems.
- Score: 57.89399576743665
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The defect detection task can be regarded as a realistic scenario of object
detection in the computer vision field and it is widely used in the industrial
field. Directly applying vanilla object detector to defect detection task can
achieve promising results, while there still exists challenging issues that
have not been solved. The first issue is the texture shift which means a
trained defect detector model will be easily affected by unseen texture, and
the second issue is partial visual confusion which indicates that a partial
defect box is visually similar with a complete box. To tackle these two
problems, we propose a Reference-based Defect Detection Network (RDDN).
Specifically, we introduce template reference and context reference to against
those two problems, respectively. Template reference can reduce the texture
shift from image, feature or region levels, and encourage the detectors to
focus more on the defective area as a result. We can use either well-aligned
template images or the outputs of a pseudo template generator as template
references in this work, and they are jointly trained with detectors by the
supervision of normal samples. To solve the partial visual confusion issue, we
propose to leverage the carried context information of context reference, which
is the concentric bigger box of each region proposal, to perform more accurate
region classification and regression. Experiments on two defect detection
datasets demonstrate the effectiveness of our proposed approach.
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