Surface defect detection of the power machinery based on deep learning

Tao Chen; Yutong Zhao; Bo Li; Tianhong Ding; Mengmeng Cui; Chongyuan Shui

doi:10.1117/12.3005919

10 October 2023 Surface defect detection of the power machinery based on deep learning

Tao Chen, Yutong Zhao, Bo Li, Tianhong Ding, Mengmeng Cui, Chongyuan Shui

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127991V (2023) https://doi.org/10.1117/12.3005919
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

With the rapid development of machine learning methods, deep learning models based on big data and computer arithmetic are widely used in various scenarios, including target detection and image recognition. In order to detect surface defects of the power machinery more rapidly and accurately, this paper investigates the performance of various convolutional neural network models in the task of detecting surface defects, identifies relatively excellent detection models based on the confusion matrix, and summarizes the more appropriate detection scenarios for different models. The results indicate that EfficientNet has good comprehensive performance in detection tasks.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tao Chen, Yutong Zhao, Bo Li, Tianhong Ding, Mengmeng Cui, and Chongyuan Shui "Surface defect detection of the power machinery based on deep learning", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127991V (10 October 2023); https://doi.org/10.1117/12.3005919

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