[1]SUN Boyan,WANG Hongyuan,LIU Qian,et al.Hybrid supervised metal surface defect detection based on multi-scale and attention[J].CAAI Transactions on Intelligent Systems,2023,18(4):886-893.[doi:10.11992/tis.202205042]
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Hybrid supervised metal surface defect detection based on multi-scale and attention

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 4 Page number: 886-893 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2023-07-15
Title:
Hybrid supervised metal surface defect detection based on multi-scale and attention
Author(s):
SUN Boyan;  WANG Hongyuan;  LIU Qian;  FENG Zundeng;  TANG Ying
School of Compute Science and Artificial Intelligence / Aliyun School of Big Data / School of Software, Changzhou University, Changzhou 213000, China
Keywords:
defect; detector; feature extraction; learnin galgorithm; learning system; image processing; metal; quality of product; deep learning
CLC:
TP391
DOI:
10.11992/tis.202205042
Abstract:
Aiming at the problem in defect detection that effective features cannot be extracted due to different shapes of defect targets in the detected samples, this paper presents a defect detection model based on deep learning, which uses an improved multi-scale feature fusion module to solve the problem of identifying defects of different sizes on the basis of controlling the amount of calculation. By introducing a non-local attention mechanism module, the model’s ability of extracting defect features is enhanced. Furthermore, mixed-supervised training is used in training to explore the relationship between the amount of annotations required by the model and the detection accuracy. This method achieves better accuracy than the state-of-the-art methods on KSDD, KSDD2, and STEEL datasets, and can extract discriminative features for different types of defects. Compared with the state-of-the-art fully supervised and unsupervised methods, the average accuracy improvement on the dataset is 0.8% and 11%.
References:
[1] 陶显, 侯伟, 徐德. 基于深度学习的表面缺陷检测方法综述[J]. 自动化学报, 2021, 47(5): 1017–1034
TAO Xian, HOU Wei, XU De. A survey of surface defect detection methods based on deep learning[J]. Acta automatica sinica, 2021, 47(5): 1017–1034
[2] TABERNIK D, ?ELA S, SKVAR? J, et al. Segmentation-based deep-learning approach for surface-defect detection[J]. Journal of intelligent manufacturing, 2020, 31(3): 759–776.
[3] KUMAR A. Computer-vision-based fabric defect detection: a survey[J]. IEEE transactions on industrial electronics, 2008, 55(1): 348–363.
[4] ABDI H, WILLIAMS L J. Principal component analysis[J]. Wiley interdisciplinary reviews:computational statistics, 2010, 2(4): 433–459.
[5] 虞祖耀, 王洪元, 张继. 基于机器视觉的织布瑕疵在线检测[J]. 计算机工程与设计, 2016, 37(10): 2851–2856
YU Zuyao, WANG Hongyuan, ZHANG Ji. On-line detection of weaving defects based on machine vision[J]. Computer engineering and design, 2016, 37(10): 2851–2856
[6] FUKUSHIMA K, MIYAKE S. Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition[M]. Berlin: Springer Berlin Heidelberg, 1982: 267?285.
[7] PARK J K, KWON B K, PARK J H, et al. Machine learning-based imaging system for surface defect inspection[J]. International journal of precision engineering and manufacturing-green technology, 2016, 3(3): 303–310.
[8] KYEONG K, KIM H. Classification of mixed-type defect patterns in wafer Bin maps using convolutional neural networks[J]. IEEE transactions on semiconductor manufacturing, 2018, 31(3): 395–402.
[9] HE Yu, SONG Kechen, MENG Qinggang, et al. An end-to-end steel surface defect detection approach via fusing multiple hierarchical features[J]. IEEE transactions on instrumentation and measurement, 2020, 69(4): 1493–1504.
[10] LI Feng, XI Qinggang. DefectNet: toward fast and effective defect detection[J]. IEEE transactions on instrumentation and measurement, 2021, 70: 1–9.
[11] GE Ce, WANG Jing, WANG Jingyu, et al. Towards automatic visual inspection: a weakly supervised learning method for industrial applicable object detection[J]. Computers in industry, 2020, 121: 103232.
[12] LI Qizhu, ARNAB A, TORR P H S. Weakly- and semi-supervised panoptic segmentation[M]. Computer Vision - ECCV 2018. Cham: Springer International Publishing, 2018: 106?124.
[13] SALEH F, ALIAKBARIAN M S, SALZMANN M, et al. Built-in foreground/background prior for weakly-supervised semantic segmentation[M]. Computer Vision-ECCV 2016. Cham: Springer International Publishing, 2016: 413?432.
[14] MINHAS M S, ZELEK J. Semi-supervised anomaly detection using autoencoders[J]. Journal of computational vision and imaging systems, 2019: 03674.
[15] BERGMANN P, L?WE S, FAUSER M, et al. Improving unsupervised defect segmentation by applying structural similarity to autoencoders[EB/OL]. (2018-07-05)[2022-05-25]. https://arxiv.org/abs/1807.02011.
[16] BERGMANN P, FAUSER M, SATTLEGGER D, et al. Uninformed students: student-teacher anomaly detection with discriminative latent embeddings[C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 4182?4191.
[17] XIN Zihao, WANG Hongyuan, QI Pengyu, et al. Printed surface defect detection model based on positive samples[J]. Computers, materials & continua, 2022, 72(3): 5925–5938.
[18] SCHLEGL T, SEEBOCK P, WALDSTEIN S M, et al. F-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks[J]. Medical image analysis, 2019, 54: 30–44.
[19] BOZIC J, TABERNIK D, SKOCAI D. Mixed supervision for surface-defect detection: from weakly to fully supervised learning[J]. Computers in industry, 2021, 129: 103459.
[20] MNIH V, HEESS N, GRAVES A, et al. Recurrent models of visual attention[EB/OL]. (2014-06-24)[2022-05-25]. https://arxiv.org/abs/1406.6247.
[21] XU K, BA J L, KIROS R, et al. Show, attend and tell: neural image caption generation with visual attention[C]//Proceedings of the 32nd International Conference on International Conference on Machine Learning-Volume 37. New York: ACM, 2015: 2048?2057.
[22] WANG Xiaolong, GIRSHICK R, GUPTA A, et al. Non-local neural networks[C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 7794?7803.
[23] DONG Hongwen, SONG Kechen, HE Yu, et al. PGA-net: pyramid feature fusion and global context attention network for automated surface defect detection[J]. IEEE transactions on industrial informatics, 2020, 16(12): 7448–7458.
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