Automatic pixel‐level crack detection with multi‐scale feature fusion for slab tracks

The segmentation accuracy of bridge crack images is influenced by high‐frequency light, complex scenes, and tiny cracks. Therefore, an integration–competition network (complex crack segmentation network [CCSNet]) is proposed to address these problems. First, a grayscale‐oriented adjustment algorithm is proposed to solve the high‐frequency light problem. Second, an integration–competition mechanism is proposed to detach complex backgrounds and grayscale features of cracks. Finally, a tiny attention mechanism is proposed to extract the shallow features of tiny cracks. CCSNet outperforms seven state‐of‐the‐art crack segmentation methods in both generalization and comparison experiments on self‐built dataset and four public datasets. It also achieved excellent performance in practical bridge crack tests. Therefore, CCSNet is an effective auxiliary method for lowering the cost of bridge safety detection.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Sun,

Yang,

Zhou

et al. 2023

“…Li et al, 2023;Ma et al, 2022), tunnel structures (Marasco et al, 2022;Rosso et al, 2023), and pavements (Rodriguez-Lozano et al, 2023;L. Zhao et al, 2022), among others (Ye et al, 2023). These AI methods provide promising solutions by leveraging their capabilities in data analysis, pattern recognition, and automated decision-making.…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based automatic classification of three‐level surface information in bridge inspection

Zhang,

Shen,

Lin

et al. 2023

Bridge inspection ensures that in‐service bridges are managed and maintained in conformity. To enhance the accuracy and efficiency of bridge inspection, an automatic hierarchical model is proposed, which enables the classification and correlation of bridge surface images at three levels, namely, at the structure, component, and defect type level. Thus, the impact of both the defect types and the affected components on bridge safety can be simultaneously considered. The proposed model uses a group of sub‐models instead of the common flat network to realize the multiple tasks, which is advantageous in accuracy, training simplicity, and scalability. The classification accuracy of the hierarchical model in three levels has reached 96%, 92%, and 81%. Results demonstrate the effectiveness of the proposed method in the classification of multi‐scale targets. This study may provide a new strategy for developing a systematic and easily adaptable detection framework for practical bridge engineering.

“…W. Ye et al. (2022, 2023) realized rapid detection of apparent cracks in rail concrete based on deep learning and developed a systematic pixel‐level crack segmentation–quantification method suited for nighttime detection of slab tracks. Q. Wang et al.…”

Section: Introductionmentioning

confidence: 99%

“…Tong et al (2023) proposed a novel anchor-adaptive railway track detection network realizing full-angle railway track detection for the UAV aerial images taken from arbitrary viewing angles. W. Ye et al (2022Ye et al ( , 2023 realized rapid detection of apparent cracks in rail concrete based on deep learning and developed a systematic pixel-level crack segmentation-quantification method suited for nighttime detection of slab tracks. Q.…”

mentioning

confidence: 99%

Intelligent recognition of defects in high‐speed railway slab track with limited dataset

Cai,

Tang,

Pan

et al. 2023

During the regular service life of high‐speed railway (HSR), there might be serious defects in the concrete slabs of the infrastructure systems, which may further significantly affect public transportation safety. To address these serious issues and fulfill the regular functions of HSR, the traditional methods for railway engineers involve carrying out regular on‐site inspections manually or by semi‐automatic inspection vehicles, and conducting timely corresponding repairing approaches and maintenance, where these methods are time‐consuming and dangerous. In recent years, machine learning methods have been widely applied to the intelligent and automatic detection of severe defects in HSR. Currently, one of the most serious problems is the lack of sufficient high‐quality data for model training, resulting in low recognition accuracy in HSR defects. To solve this problem, this paper proposed an intelligent recognition of defects in concrete slabs of HSR based on a few‐shot learning model, that is, an artificial intelligence model based on limited data size, which recognizes three service conditions of concrete slabs in HSR: cracks, track board gaps, and unbroken state. Lightweight few‐shot learning models specifically designed for HSR detection were proposed. Experiments were conducted to compare the performances of different lightweight‐designed models, including accuracy, parameter quantity, and testing time. Results showed that the optimum model can fast and satisfactorily recognize the defects in HSR with a very limited data size of 10 samples for each training category, with a satisfactory accuracy of 73.9% in the test dataset with 20 samples for each category, parameter amounts of 2.8 million, and a testing time of 2.2 s per image. This study provides a reference for the automatic recognition of defects in HSR by railway engineers with insufficient samples.