Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

Li Wei (), Mahmud Iwan Solihin (), Sarah ‘Atifah Saruchi (), Winda Astuti (), Lim Wei Hong and Ang Chun Kit
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Li Wei: Guangxi University of Science and Technology
Mahmud Iwan Solihin: UCSI University
Sarah ‘Atifah Saruchi: Universiti Malaysia Pahang Al-Sultan Abdullah
Winda Astuti: BINUS ASO School of Engineering, Bina Nusantara University
Lim Wei Hong: UCSI University
Ang Chun Kit: UCSI University

SN Operations Research Forum, 2024, vol. 5, issue 3, 1-71

Abstract: Abstract High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation.

Keywords: Defect detection; Anomaly detection; Computer vision; High-precision cylindrical parts; Optical illumination; Image processing; Deep learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s43069-024-00337-5

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