Cited by 126Industrial Vision Systems and Defect DetectionAdvanced Neural Network ApplicationsInfrastructure Maintenance and Monitoring

A Two-Stage Industrial Defect Detection Framework Based on Improved-YOLOv5 and Optimized-Inception-ResnetV2 Models

Open access
Li, Zhuang, Tian, Xincheng, Liu, Xin, Liu, Yan, Shi, Xiaorui·2022·Applied Sciences

Abstract

Aiming to address the currently low accuracy of domestic industrial defect detection, this paper proposes a Two-Stage Industrial Defect Detection Framework based on Improved-YOLOv5 and Optimized-Inception-ResnetV2, which completes positioning and classification tasks through two specific models. In order to make the first-stage recognition more effective at locating insignificant small defects with high similarity on the steel surface, we improve YOLOv5 from the backbone network, the feature scales of the feature fusion layer, and the multiscale detection layer. In order to enable second-stage recognition to better extract defect features and achieve accurate classification, we embed the convolutional block attention module (CBAM) attention mechanism module into the Inception-ResnetV2 model, then optimize the network architecture and loss function of the accurate model. Based on the Pascal Visual Object Classes 2007 (VOC2007) dataset, the public dataset NEU-DET, and the optimized dataset Enriched-NEU-DET, we conducted multiple sets of comparative experiments on the Improved-YOLOv5 and Inception-ResnetV2. The testing results show that the improvement is obvious. In order to verify the superiority and adaptability of the two-stage framework, we first test based on the Enriched-NEU-DET dataset, and further use AUBO-i5 robot, Intel RealSense D435 camera, and other industrial steel equipment to build actual industrial scenes. In experiments, a two-stage framework achieves the best performance of 83.3% mean average precision (mAP), evaluated on the Enriched-NEU-DET dataset, and 91.0% on our built industrial defect environment.

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  • Environmental interference in actual production, such as uneven lighting, light reflection, noise, motion blur, false defects, and low contrast background clutter, causes recognition effect to be less than ideal.

    “In addition, in the actual production environment, the interference of the environment is serious such as uneven lighting, light reflection, noise, motion blur, the existence of many false defects, and a low contrast of background clutter, resulting in the recognition effect being often less than ideal [8].”

    From Steel Surface Defect Recognition: A Survey (2022) · cites this paper as [8]

    Section 2 (Related Work; page unavailable) says background information can interfere with defect features and reduce recognition accuracy. The comparison of detection schemes also describes difficulty identifying fuzzy, dark, or illegible defects. The paper does not attribute poor recognition to the cited sentence’s specific conditions—uneven lighting, reflections, noise, motion blur, or false defects—or establish that recognition in actual production is often less than ideal. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 8

  1. Bo, X. Knowledge of ultra-high-strength steel used in aerospace and other industries. Heat Treat. 2019, 34, 61-63.
  2. Zhang, M.; Zheng, H.; Ni, H.; Wang, Y.; GUO, C. Ultrasound image defect classification based on genetic algorithm optimized support vector machine. Acta Metrol. 2019, 40, 887-892.
  3. Xu, Q.; Zhang, X.; Yu, S.; Chen, Q.; Liu, W. A random forest classification algorithm for polarized SAR images with comprehensive multi-features. J. Remote Sens. 2019, 23, 685-694.
  4. Wu, P.; Lu, T.; Wang, Y. Machine vision and nondestructive inspection of steel plate surface defects. Nondestruct. Test. 2000, 22, 13-16.
  5. Guo, Y.; Deng, X.; Gao, C. Surface defect automatic detection algorithm based on principal component analysis. Comput. Eng. 2013, 39, 216-219.
  6. Liu, X.-P.; Li, G.; Liu, L.; Wang, Z. Improved YOLOV3 target recognition algorithm based on adaptive eged optimization. Microelectron. Comput. 2019, 36, 59-64.
  7. Rosebrock, A. Deep Learning for Computer Vision with Python Practitioner Bundle. Md. PyImage Search 2017, 1, 57-69.
  8. Akiba, T.; Suzuki, S.; Fukuda, K. Extremely large minibatch SGD: Training ResNet-50 on ImageNet in 15 minutes. arXiv 2017, arXiv:1711.04325. · arXiv

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Authors
Li, Z., Tian, X., Liu, X., Liu, Y., & Shi, X.
Affiliations
  • Shandong University (CN)
  • Nankai University (CN)
  • China National Heavy Duty Truck Group (China) (CN)
Corresponding author
Tian, Xincheng
Venue
Applied Sciences
Publisher
MDPI AG
Published
2022-01-14
Volume
12
Issue
2
Pages
834
ISSN
2076-3417 (ISSN-L)
OpenAlex
W4206765025
Cited by
126
References
21
Citations by year
2026: 12 · 2025: 24 · 2024: 29 · 2023: 42 · 2022: 19
FWCI
13.09
Type
journal article
Language
en
Open access
Locations
  • Applied Sciences (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
Keywords
industrial defect detection, steel surface defects, two-stage detection framework, InceptionResNetV2, CBAM attention mechanism, defect classification, NEU-DET dataset

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