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Machine vision-based angle-arrayed imaging and two-stage deep learning for gear defect detection
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AI
DOI：10.1364/AO.579975.png)
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Abstract
En 中文
Industrial gears are highly susceptible to surface defects under high-load and high-speed operating conditions, which can lead to reduced service life or even complete machine failure. However, complex operating environments pose significant challenges to achieving high-precision online defect detection. This paper proposes an online detection method integrating synchronous in situ tooth surface imaging with a two-stage deep segmentation strategy. The system achieves complete coverage of tooth surface images through motion-aligned imaging mechanisms and employs a two-stage cascaded architecture to enhance detection performance: the first stage rapidly segments the active tooth surface area, while the second stage utilizes an improved U-shaped dual-resolution network (UDDRNet) for precise identification of minute defects. Experimental results demonstrate that this method achieves 87.42% mIoU, 91.89% Recall, and 92.15% F1 scores while maintaining real-time performance, significantly outperforming single-stage methods and existing semantic segmentation models. These findings not only validate the high accuracy and practicality of the proposed method in complex industrial scenarios but also provide a scalable technical pathway for intelligent quality monitoring of critical industrial components. (c) 2026 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.
Keywords:
gear defect detection
machine vision
deep learning
surface imaging
industrial quality monitoring
Journal
A
IF:
1.7
Papers:
968
Citations:
5.1W
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