Bridge Damage Detection and Recognition Based on Deep Learning

Chen, Xiuxin; Yang, Ye; Zhang, Xue; Yu, Chongchong

doi:10.1088/1742-6596/1626/1/012151

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…The experimental results showed that the SPP module could improve mAP by 1.3%; transfer learning could improve mAP by 20.9%. Chen et al 54 used deformable convolution to extract more accurate features based on YOLOV3. Similarly, transfer learning was also used to improve the identification accuracy of rust, collapse, cracks and weeds on the bridge surface.…”

Section: Artificial Intelligence Solutions For Bridge Damage Detectionmentioning

confidence: 99%

Review of artificial intelligence-based bridge damage detection

Zhang

Yuen

2022

Advances in Mechanical Engineering

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Bridges are often located in harsh environments and are thus extremely susceptible to damage. If the initial damage is not detected in time, it can develop further causing safety hazards. Therefore, accurate detection of bridge damage is an important topic. In recent years, artificial intelligence technology has been developed rapidly, especially machine learning algorithms, which have shown amazing results in various fields while it also received attention in bridge inspection. This paper summarizes the progress of bridge damage detection research related to artificial intelligence techniques between 2015 and 2021. For structural health monitoring, sensing data is the basis for various data processing methods. The strength and weakness of the sensing data itself directly affect the effectiveness of subsequent processing methods. As a result, this paper classifies bridge damage detection studies into six categories from the types of sensing data: visual image, point cloud, infrared thermal imaging, ground-penetrating radar, vibration response, and other types of data. These six types of damage detection methods were reviewed and summarized respectively. Finally, challenges and future trends were discussed.

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Section: Artificial Intelligence Solutions For Bridge Damage Detectionmentioning

confidence: 99%

Review of artificial intelligence-based bridge damage detection

Zhang

Yuen

2022

Advances in Mechanical Engineering

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“…After testing the model, they evaluated that algorithm can successfully detect cracks in different kind of images. Chen et al (2020) proposed a model for damage detection in concrete bridges using a deep learning model. Their model is based on the technique of transfer learning, taking an existing state-of-the-art object detection algorithm YOLOv3 is used for object localizing.…”

Section: Concrete Structures Condition Assessmentmentioning

confidence: 99%

Automatic assessment of roofs conditions using artificial intelligence (AI) and unmanned aerial vehicles (UAVs)

Alzarrad

Awolusi²,

Hatamleh³

et al. 2022

Front. Built Environ.

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Building roof inspections must be performed regularly to ensure repairs and replacements are done promptly. These inspections get overlooked on sloped roofs due to the inefficiency of manual inspections and the difficulty of accessing sloped roofs. Walking a roof to inspect each tile is time-consuming, and as the roof slope increases, this difficulty increases the time needed for an inspection. Moreover, there is an intrinsic safety risk involved. Falls from roofs tend to cause severe and expensive injuries. The emergence of new sensing technologies and artificial intelligence (AI) such as high-resolution imagery and deep learning has enabled humans to move beyond the concept of using manual labor in damage assessments. It has brought significant advantages in the field of safety management, and it can be a substitute for the traditional assessment of roofs. This study uses unmanned aerial vehicles (UAVs) and deep learning technology to perform sloped roof inspections effectively, thus eliminating the safety risk involved in traditional manual inspections. This study utilizes UAVs and deep learning to automatically collect and classify roof imagery to identify missing shingles on the roof. The proposed research can help real estate agents, insurance companies, and others make better and more informed decisions about roof conditions. Future research could be refining the model to deal with different types of defects in addition to missing shingles.

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“…X. Chen et al. (2020) proposed a deep learning method called DT‐YOLOv3 to recognize and detect bridge damage, while Qiao et al. (2021) presented a framework called EMA‐DenseNet that can automatically detect multiple types of damage, including cracks and exposed steel bars on a bridge.…”

Section: Introductionmentioning

confidence: 99%

Improving visual question answering for bridge inspection by pre‐training with external data of image–text pairs

Kunlamai,

Yamane,

Suganuma

et al. 2023

Computer aided Civil Eng

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This paper explores the application of visual question answering (VQA) in bridge inspection using recent advancements in multimodal artificial intelligence (AI) systems. VQA involves an AI model providing natural language answers to questions about the content of an input image. However, applying VQA to bridge inspection poses challenges due to the high cost of creating training data that requires expert knowledge. To address this, we propose leveraging existing bridge inspection reports, which already include image–text pairs, as external knowledge to enhance VQA performance. Our approach involves training the model on a large collection of image–text pairs, followed by fine‐tuning it on a limited amount of training data specifically designed for the VQA task. The results demonstrate a significant improvement in VQA accuracy using this approach. These findings highlight the potential of AI models for VQA as valuable tools for assessing the condition of bridges.

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Bridge Damage Detection and Recognition Based on Deep Learning

Cited by 5 publications

References 2 publications

Review of artificial intelligence-based bridge damage detection

Review of artificial intelligence-based bridge damage detection

Automatic assessment of roofs conditions using artificial intelligence (AI) and unmanned aerial vehicles (UAVs)

Improving visual question answering for bridge inspection by pre‐training with external data of image–text pairs

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