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4 September 2024 Fan blade surface defect recognition algorithm based on improved ResNet50
Yilin Miao, Yuhong Tang, Xiaojian Pan, Long Liu, Yunfeng Zhai, Jianjun Li
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Proceedings Volume 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024); 1325936 (2024) https://doi.org/10.1117/12.3039335
Event: Fourth International Conference on Automation Control, Algorithm, and Intelligent Bionics (ICAIB 2024), 2024, Yinchuan, China
Abstract
At present, the traditional recognition methods such as naked eye recognition and machine vision are mainly used for the surface defect image recognition of fan blades, and their robustness is poor. In order to improve the recognition accuracy of fan blade surface defects, an improved ResNet50 algorithm is proposed in this paper. 1416 fan blade images were captured by UAV, and the data set of fan blade surface defects was established through screening, classification and data expansion. In order to verify the validity of the experiment, the data sets of sand holes and oil pollution defects of fan blades were subsequently supplemented in this experiment, and the same data set processing method was used. With ResNet50 as the backbone feature extraction network, the channel attention mechanism SENet is introduced to enhance the reuse rate of useful features and suppress useless features. In order to reduce the risk of inactivation of some neurons using ReLu activation function, this paper uses Leaky-ReLU activation function to redesign the residual block. The experimental results show that the classification accuracy of the algorithm on the self-made data is up to the baseline model, which can meet the actual demand.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yilin Miao, Yuhong Tang, Xiaojian Pan, Long Liu, Yunfeng Zhai, and Jianjun Li "Fan blade surface defect recognition algorithm based on improved ResNet50", Proc. SPIE 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024), 1325936 (4 September 2024); https://doi.org/10.1117/12.3039335
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KEYWORDS
Fluctuations and noise
Data modeling
Defect detection
Image enhancement
Detection and tracking algorithms
Image classification
Image processing
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