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

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 F1 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.

Identification of Location and Geometry of Invisible Internal Defects in Structures using Deep Learning and Surface Deformation Field

“…Although several researchers have explored crack detection in structures such as roads, [ 40 ] aeroengine blades, [ 41 ] pipelines, [ 42 ] bridges, [ 43 ] tunnels, [ 44 ] and human limbs, [ 45 ] these studies have been limited to surface cracks. The proposed method is an advancement over existing methods in that it can identify invisible subsurface defects and reveal the geometric information at a low cost for real‐time monitoring of structures in their natural states.…”

An integration–competition network for bridge crack segmentation under complex scenes

“…Since the middle of the 20th century, the bridge construction scale has become larger (Johansen et al., 2023; Kaur et al., 2023; Ma et al., 2023; Rafiei & Adeli, 2017). It is vital to conduct periodical detections on the security and durability of bridges (Mirzaei & Adeli, 2019; Ni et al., 2018; Okazaki et al., 2023; Shim et al., 2023; Sirca & Adeli, 2018).…”