Cross-domain pipeline defect detection under strong interference and limited data based on UAV vision technology

Due to the harsh service environment, oil and gas pipelines are susceptible to corrosion defects. Unmanned aerial vehicle (UAV) vision technology is a non-destructive testing method for in-service pipelines. Nevertheless, it is challenging to provide an accurate defect detection under environmental strong interference. Additionally, limited data can reduce the detection accuracy and model generalisation capability. In this paper, a cross-domain defect detection method for oil and gas pipelines is proposed based on UAV vision technology. Firstly, an experimental setup with the UAV inspection is designed to collect the pipeline images. To alleviate domain shift posed by variations in working conditions, an image preprocessing module based on adaptive Retinex algorithm combined with Unet and attention mechanism is developed. Then, a cross-domain detection module that leverages residual autoencoder and pseudo-label strategy is established to achieve accurate defect detection under limited labelled data. Finally, the comparative experiments are conducted to verify the domain generalisation performance for strong interference of low light and background noise. The experimental results demonstrate that the proposed method outperforms state-of-the-art methods in terms of cross-domain transfer tasks. This study makes a significant contribution to enhance the preventive maintenance of oil and gas pipelines.