KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 14, NO. 3, Mar. 2020 1086 Copyright ⓒ 2020 KSII http://doi.org/10.3837/tiis.2020.03.010 ISSN : 1976-7277 Oil Pipeline Weld Defect Identification System Based on Convolutional Neural Network Jiaze Shang 1* , Weipeng An 1 , Yu Liu 1 , Bang Han 1 , Yaodan Guo 1 1 School of computer science and technology, Henan Polytechnic University Jiaozuo Henan, 454000, China [e-mail: shangjiazecn@163.com] *Corresponding author: Jiaze Shang Received May 5, 2019; revised October 9, 2019; accepted November 24, 2019; published March 31, 2020 Abstract The automatic identification and classification of image-based weld defects is a difficult task due to the complex texture of the X-ray images of the weld defect. Several depth learning methods for automatically identifying welds were proposed and tested. In this work, four different depth convolutional neural networks were evaluated and compared on the 1631 image set. The concavity, undercut, bar defects, circular defects, unfused defects and incomplete penetration in the weld image 6 different types of defects are classified. Another contribution of this paper is to train a CNN model "RayNet" for the dataset from scratch. In the experiment part, the parameters of convolution operation are compared and analyzed, in which the experimental part performs a comparative analysis of various parameters in the convolution operation, compares the size of the input image, gives the classification results for each defect, and finally shows the partial feature map during feature extraction with the classification accuracy reaching 96.5%, which is 6.6% higher than the classification accuracy of other existing fine-tuned models, and even improves the classification accuracy compared with the traditional image processing methods, and also proves that the model trained from scratch also has a good performance on small-scale data sets. Our proposed method can assist the evaluators in classifying pipeline welding defects. Keywords: pipeline x-ray welding image; automatic identification; feature extraction; convolution neural network; convolution operation