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Diyu Guan

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Proceedings Article | 13 June 2024 Paper

Recognition of damaged pipeline welds based on deep learning

Diyu Guan, Zhiyong Xin, Yinxin Tao, Peipei Sun

Proceedings Volume 13180, 131805M (2024) https://doi.org/10.1117/12.3034184

KEYWORDS: Convolution, Detection and tracking algorithms, Deep learning, Object detection, Education and training, Convolutional neural networks, Computing systems, Target recognition, Roads, Process control

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Welding technology is one of the key processing technologies in the field of mechanical manufacturing and engineering construction. With the application of artificial intelligence in welding equipment and process control technology, the degree of automation, control accuracy and quality stability of welding technology have been improved to a certain extent. However, there is a lack of effective supervision measures for pipeline welding quality to ensure the smooth progress of process processing. In this paper, the improved YOLOv8 algorithm is used to identify and weld, and the original loss function is replaced by the SIoU loss function, which enhances the iterative speed and detection progress of the model. In addition, the depth separable convolution is used to replace the original void convolution, which makes up for the problem of information loss when the size object may exist, greatly improves the recognition ability and accuracy of the weld quality of the model, and lays a foundation for the improvement of the welding ability of the welding robot.

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