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A Two-Stage Industrial Defect Detection Framework Based on Improved-YOLOv5 and Optimized-Inception-ResnetV2 Models
Open accessAbstract
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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“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]
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Metadata
- 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
- gold
- doi.org
- Licence
- 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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