Transformer‐optimized generation, detection, and tracking network for images with drainage pipeline defects

变压器 管道(软件) 管道运输 卷积神经网络 计算机科学 排水 特征提取 人工神经网络 模式识别(心理学) 人工智能 计算机视觉 实时计算 工程类 电气工程 电压 环境工程 生物 生态学 程序设计语言
作者
Duo Ma,Hongyuan Fang,Niannian Wang,Hongfang Lü,John C. Matthews,Chao Zhang
出处
期刊:Computer-aided Civil and Infrastructure Engineering [Wiley]
卷期号:38 (15): 2109-2127 被引量:50
标识
DOI:10.1111/mice.12970
摘要

Abstract Regular detection of defects in drainage pipelines is crucial. However, some problems associated with pipeline defect detection, such as data scarcity and defect counting difficulty, need to be addressed. Therefore, a Transformer‐optimized generation, detection, and counting method for drainage‐pipeline defects was established in this paper. First, a generation network called Trans‐GAN‐Cla was developed for data augmentation. A classification network was trained to improve the quality of the generated images. Second, a detection and tracking model called Trans‐Det‐Tra was developed to track and count the number of defects. Third, the feature extraction capability of the proposed method was improved by leveraging Transformers. Compared with some well‐known convolutional neural network‐based methods, the proposed network achieved the best classification and detection accuracies of 87.2% and 87.57%, respectively. Furthermore, the F 1 scores were 87.7% and 91.9%. Finally, two pieces of onsite videos were detected and tracked, and the numbers of misalignments and obstacles were accurately counted. The results indicate that the established Transformer‐optimized method can generate high‐quality images and realize the high‐accuracy detection and counting of drainage pipeline defects.
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