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21 | 05 | 2024
10.14489/vkit.2024.03.pp.030-036

DOI: 10.14489/vkit.2024.03.pp.030-036

Васильев М. Е., Коськин А. В., Шалимов А. С.
АВТОМАТИЗАЦИЯ ОБНАРУЖЕНИЯ ДЕФЕКТОВ ПОВЕРХНОСТЕЙ ИЗДЕЛИЙ НА ОСНОВЕ СВЕРТОЧНЫХ НЕЙРОННЫХ СЕТЕЙ
(с. 30-36)

Аннотация. Рассмотрено применение сверточных нейронных сетей в целях автоматизации обнаружения дефектов поверхностей изделий. Основной фокус исследования сосредоточен на алгоритме обнаружения объектов сверточных нейронных сетей YOLOv5 и его улучшенной версии YOLOv5s-KEB. Эти алгоритмы предлагают эффективное решение для точного и быстрого распознавания дефектов, что позволяет производителям сократить время анализа изделий и повысить качество своей продукции.

Ключевые слова:  автоматизация; обнаружение дефектов; поверхности изделий; алгоритм YOLOv5; сверточные нейронные сети.

 

Vasilev M. E., Koskin A. V., Shalimov A. S.
AUTOMATED DEFECT DETECTION ON PRODUCT SURFACES BASED ON CONVOLUTIONAL NEURAL NETWORKS
(pp. 30-36)

Abstract. This study is devoted to the development and analysis of models for detecting defects on product surfaces using convolutional neural networks, such as YOLOv5 and YOLOv5s-KEB. These models are based on the use of convolutional layers, recurrent layers and attention mechanisms, which allows them to effectively detect various types of defects such as cracks, gaps, pits and scratches. The study compared various models including YOLOv5s, YOLOv5s-KEB and their modifications such as Model A, Model B and Model C. The study found that using k-means generated anchors resulted in significant improvement in inference time and accuracy of defect detection. Other methods for improving models, such as the use of the BiFPN module and the use of the ECA mechanism, were also considered. The results of the study can be useful for developing more efficient and accurate models for detecting defects on product surfaces in the manufacturing industry. They can help improve product quality control and improve product safety and reliability. In addition, the results of the study can be used to create new methods for detecting defects on product surfaces, which can be more efficient and accurate than existing methods.

Keywords: Automation; Defect detection; Product surfaces; YOLOv5 algorithm; Convolutional neural networks.

Рус

Н. А. Кузнецов, С. В. Антонов (МИРЭА – Российский технологический университет, Москва, Россия) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript  

Eng

M. E. Vasilev, A. V. Koskin, A. S. Shalimov (Oryol State University named after I. S. Turgenev, Orel, Russia) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript

Рус

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Eng

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5. Liu W., Anguelov D., Erhan D. (2016). Ssd: Single shot multibox detector. Computer Vision–ECCV 2016: 14th European Conference, Part I, 14, 21 – 37. Amsterdam, The Netherlands, October 11 – 14, 2016, Proceedings, Part I 14. Springer International Publishing, 2016. P. 21–37.
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7. Kou X., Liu S., Cheng K., Qian Y. et al. (2021). Development of a YOLO-V3-based model for detecting defects on steel strip surface. Measurement, 182.
8. Andriyanov N., Khasanshin I., Zhang C. (2022). Intelligent system for estimation of the spatial position of apples based on YOLOv3 and real sense depth camera D415. Symmetry, 14(1).
9. Tulbure A. A., Tulbure A. A., Dulf E. H. (2022). A review on modern defect detection models using DCNNs–Deep convolutional neural networks. Journal of Advanced Research, 35, 33 – 48.
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15. Liu S., Qi L., Qin H. (2018). Path aggregation network for instance segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, 8759 – 8768.
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Рус

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