Cited By
View all- Jiang XXiao PYan FLu YJin SXu M(2024)Context Mutual Evolution Network for Weakly Supervised Surface Defect DetectionPattern Recognition10.1007/978-3-031-78192-6_17(256-270)Online publication date: 4-Dec-2024
Transformer-based Encoder-Decoder Model for Surface Defect Detection
Authors: 
Xiaofeng Lu, Wentao FanAuthors Info & Claims
Xiaofeng Lu, Wentao FanAuthors Info & ClaimsPages 125 - 130
Abstract
Recently, deep learning approaches have been gaining popularity in industrial quality control (e.g. surface defect detection), due to their ability for automatically extracting more representative features. In this paper, we propose a two-stage end-to-end approach through a Transformer-based encoder-decoder for surface defect detection. First, we develop a surface defect detection model to train the slicing of input raw images with the same final resolution of the input images and the output images, which better expands the perceptual field. After that, a 1×1 convolution layer is applied to its final layer, thus reducing the number of channels to obtain a single-channel output mask. Then, we combine this single-channel output mask with the output obtained from the last layer of the first stage as the input of the second stage decision layer. Considering different types of sample data, we design two different decision network strategies, namely: plain-up sampling and dynamic-up sampling. Our experimental studies on several publicly available datasets show that the proposed approach is general and effective in detecting defects, and we only need a relatively small number of samples to train the model, which has a good applicability in industrial practice where the sample size is normally limited.
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Published In
March 2022
240 pages
ISBN:9781450395502
DOI:10.1145/3529466
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Published: 04 June 2022
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ICIAI 2022
ICIAI 2022: 2022 the 6th International Conference on Innovation in Artificial Intelligence
March 4 - 6, 2022
Guangzhou, China
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View all- Jiang XXiao PYan FLu YJin SXu M(2024)Context Mutual Evolution Network for Weakly Supervised Surface Defect DetectionPattern Recognition10.1007/978-3-031-78192-6_17(256-270)Online publication date: 4-Dec-2024
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