Target Detection of Leakage Bubbles in Stainless Steel Welded Pipe Gas Airtightness Experiments Based on YOLOv8-BGA

Gas-tightness experiment is an effective means to detect leakage of stainless steel welded pipe, and the vision-based bubble recognition algorithm can effectively improve the efficiency of gas-tightness detection. This study proposed a new detection network of YOLOv8-BGA using the YOLOv8 model as a baseline, which can achieve effective identification of leakage bubbles and bubble images are collected under different lighting conditions in a practical industrial inspection environment to create a bubble dataset. Firstly, a C2f_BoT module was designed to replace the C2f module in the backbone network, which improved the feature extraction capability of the model; secondly, the convolutional layer of the neck network was replaced by using the GSConv module, which achieved the model lightweighting; thirdly, the C2f-BM attention mechanism was added before the detection layer, which effectively improved the model performance; finally, the WIoU was used to improve the loss function, which improved the detrimental effect of small bubbles of low-quality samples in the dataset on the gradient, and significantly improved the convergence speed of the network. The experimental results showed that the average leakage bubble detection accuracy of the YOLOv8-BGA model algorithm reached 97.7%, which improved by 5.3% compared with the baseline, and meets the needs of practical industrial inspection.

@article{Hou2025,
  title     = {Target Detection of Leakage Bubbles in Stainless Steel Welded Pipe Gas Airtightness Experiments Based on YOLOv8-BGA},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Huaishu Hou and Zikang Chen and Chaofei Jiao},
  doi       = {10.14569/IJACSA.2025.0160264},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160264}
}