## 1 Introduction  
In the past decades, polyethylene (PE) pipelines have been widely used in natural gas networks around the world because of their flexibility and corrosion resistance [1]. Therefore, the long-term performance of PE pipes and their materials is of great concern to date [2]. According to international natural gas pipeline accident statistics, natural gas pipeline defects are frequently caused by localized corrosion [3], operator mistakes, defective materials, and construction flaws [4]. As the use of pipelines for transporting hazardous substances becomes more popular worldwide, the possibility of major accidents caused by pipeline failures is gradually increasing [5]. For example, the explosion caused by a gas pipeline leak in a residential building in Slovakia in 2019, which killed at least seven people, reminds us that gas pipelines must be checked regularly [6]. There are many nondestructive inspection methods for gas pipelines in practice (e.g., ultrasonic-based sensors, laser-based systems, etc.), and compared to other inspection techniques commonly used for PE pipelines, due to their distinctive benefits of intuitiveness, accuracy and convenience, visual inspection techniques have been used extensively in a variety of fields [7]. Pipeline defect detection robots equipped with intra-visual inspection of image processing technology can directly collect, transmit and process images, reducing labor costs [8].  
Traditionally, automatic classification of images is carried out using extracted image features, which are used to represent unclear information in the original pixel values. Convolutional neural networks (CNN) have taken the place of that approach in recent years [9]. Image pre-processing, which includes image de-noising [10], image enhancement [11],image segmentation [12], morphological operations [13], etc., is generally performed before image classification. Based on image pre-processing, [PERSON] et al. [14] investigated an improved spline local mean decomposition (ISLMD), proposed to be CNN-based and enabling noise reduction of images to locate pipe leakage locations. [PERSON] et al. [15] proposed a sewer multi-defect detection system based on CNN-style GAN-SDM image pre-processing, and the proposed model's average accuracy and macro F1 score were 95.64% and 0.955, respectively. [PERSON] et al. [16] presented a small and simple probe design that was used to check small-bore pipes for defects. [PERSON] et al. [17] developed a visual recognition-based pipeline fault detection algorithm. It is capable of both autonomous localization and pipeline fault detection.  
Several frameworks based on the original CNN structure have been proposed to enhance target detection performance as a result of the advancement of CNN technology and classification, such as R-CNN [18], Fast R-CNN [19], SSD [20], YOLO series [21], etc. In this paper, a CNN-based classification framework for PE pipe defect detection is proposed, which can automatically extract the abstract features of defects for accurate classification of three defects, including cracks, fractures, and holes. In this paper, three different algorithms are applied to the existing framework and the confusion matrix is used to determine which model framework has the highest accuracy. The experimental results indicate that the highest accuracy of the test set in this paper reached 97% in the environment of TensorFlow API.