科研成果详情

题名Transformer-based Encoder-Decoder Model for Surface Defect Detection
作者
发表日期2022-03-04
会议名称6th International Conference on Innovation in Artificial Intelligence (ICIAI)
会议录名称ACM International Conference Proceeding Series
页码125-130
会议日期MAR 04-06, 2022
会议地点ELECTR NETWORK
摘要

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.

关键词deep learning industrial quality inspection surface anomaly detection transformer
DOI10.1145/3529466.3529471
URL查看来源
收录类别CPCI-S
语种英语English
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号WOS:001117761800021
Scopus入藏号2-s2.0-85131860717
引用统计
文献类型会议论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/13100
专题个人在本单位外知识产出
理工科技学院
作者单位
College of Computer Science and Technology,Huaqiao University,Xiamen,China
推荐引用方式
GB/T 7714
Lu, Xiaofeng,Fan, Wentao. Transformer-based Encoder-Decoder Model for Surface Defect Detection[C], 2022: 125-130.
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