## 1 Introduction  
In the context of \"Dual Carbon\", renewable energy is increasingly becoming a dominant component of the energy mix. Natural gas, as a clean and efficient fossil fuel, is essential for improving the energy mix and supporting the shift to cleaner energy sources. Pipeline transportation, one of the five major transportation modes, is the primary means of natural gas delivery [1, 2, 3]. Among the various pipeline materials, PE pipelines offer advantages such as cost-effectiveness, wear and corrosion resistance, and long service life. As a result, they are gradually replacing traditional steel pipelines and are widely used in the construction of gas pipeline networks [4]. PE pipelines are typically buried underground and subject to uncontrollable factors such as changes in underground temperature, foundation settlement, termite infestation, and stress effects. These conditions may develop defects such as structural aging, deformation, cracks, and dislocation at pipe joints, which can lead to serious risks such as gas leaks, fires, explosions, and other hazardous events, posing a significant threat to public safety [5; 6; 7; 8]. Therefore, regular inspection and maintenance of gas polyethylene pipelines are essential to mitigate safety risks. In gas pipeline defect detection, conventional non-destructive testing methods include ultrasonic testing, X-ray, infrared thermal imaging and machine vision methods [9]. Due to factors such as pipe material and inspection costs, ultrasonic testing, X-ray, and infrared thermal imaging are predominantly used for inspecting metal pipes [10; 11; 12]. The machine vision method, on the other hand, is fast, reduces inspection costs and workload, is unaffected by pipe material, and can be a comprehensive inspection of the internal surface of the pipe's internal surface, structural deformations, and other defects [13; 14]. In the field of machine vision, deep learning has been extensively applied to tasks such as image classification and object detection. However, data are crucial for training models, and the high cost and privacy concerns associated with collecting data on defects in gas polyethylene pipes often make it difficult to obtain and the lack of public datasets for researchers' reference. This further complicates the training of deep learning models. Therefore, using data augmentation to increase the number of defect samples is important to improve the accuracy of internal defect detection of gas polyethylene pipelines. Data augmentation techniques offer an effective solution to the problem of limited samples by processing and expanding the existing data, enabling the limited data to generate value equivalent to that of a larger datasets. This enhances the model's learning ability and is primarily categorized into two types: supervised and unsupervised [15]. Common methods for supervised data augmentation include geometric transformations, color transformations, CutMix, Mixup, Mosaic, and SMOTE [16]. Geometric transformations involve operations such as rotation, scaling, translation, flipping, and cropping. Color transformations modify the color attributes of samples, including brightness, contrast, saturation, hue, and noise addition. CutMix generates a new image by combining two images: it cuts both images at a specific ratio and exchanges their corresponding sections [17]. Mixup creates a new sample by linearly blending two images at the pixel level and assigning class labels proportionally [18]. The Mosaic method, similar to CutMix, simulates complex scenarios by combining multiple images in a defined layout to generate a new image [19]. The SMOTE method addresses class imbalance by synthesizing new samples from the minority class, thereby improving the classification model's ability to recognize the minority class [20]. Although these methods can effectively expand the datasets, they have certain limitations.