Resonator defect detection based on single image generative adversarial network and YOLO

Liuyihui Qian; Xiaoqing Xu; Xiaojun Liu; Ning Zhang; Juan Wu; Min Xia

doi:10.1117/12.3039350

5 August 2024 Resonator defect detection based on single image generative adversarial network and YOLO

Liuyihui Qian, Xiaoqing Xu, Xiaojun Liu, Ning Zhang, Juan Wu, Min Xia

Author Affiliations +

Proceedings Volume 13226, Third International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024); 1322619 (2024) https://doi.org/10.1117/12.3039350
Event: 3rd International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024), 2024, Changsha, China

Abstract

In the manufacture of ceramic resonators, defects on surfaces can directly affect the working stability of resonator products. Due to extreme scarcity of abnormal resonator image samples containing defect textures, traditional computer vision algorithms have difficulty learning the key data distribution characteristics of abnormal resonators, leading to poor detection results. To solve this problem, this paper proposes a resonator defect detection method based on single image generation and deep learning classification. By training SinGAN model on a single real resonator defect image, we learn the spatial and textural features on the image and then we can utilize the trained model to create seemingly realistic fake surface crack images from several sketch map drawings, therefore effectively increasing the number of abnormal resonator samples. We train a YOLOv3 object detection model on the enlarged dataset only containing fake abnormal resonator samples and try to detect cracks on new real defect resonators. Experiments show that our proposed method has better image generation quality compared with previous methods and the YOLOv3 model bounds the real cracks successfully, proving that using a single defect sample to detect more and more defective images is feasible. More importantly, our method can be commonly used in classification and detection tasks of industrial products which have a similar data distribution.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Liuyihui Qian, Xiaoqing Xu, Xiaojun Liu, Ning Zhang, Juan Wu, and Min Xia "Resonator defect detection based on single image generative adversarial network and YOLO", Proc. SPIE 13226, Third International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024), 1322619 (5 August 2024); https://doi.org/10.1117/12.3039350

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KEYWORDS

Resonators

Object detection

Defect detection

Data modeling

Statistical modeling

Image quality

Deep learning

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