Bridge Crack Semantic Segmentation Based on Improved Deeplabv3+

Fu, Huixuan; Meng, Dan; Li, Wenhui; Wang, Yuchao

doi:10.3390/jmse9060671

Cited by 68 publications

(30 citation statements)

References 38 publications

(25 reference statements)

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“…Compared to the recent image-based semantic segmentation methods, SNEPointNet++ results in 1-6% (Hoskere et al, 2020;Fu et al, 2021;Wang et al, 2022) and 1% higher IoU (Hoskere et al, 2020) in terms of crack and spall detection, respectively. Although Lee et al (2019) trained a model with the highest precision in crack semantic segmentation, SNEPointNet++ leads to 19% higher recall.…”

Section: Discussionmentioning

confidence: 97%

Point Cloud-Based Concrete Surface Defect Semantic Segmentation Using Modified PointNet++

Bolourian¹,

Hammad²,

Ghelman³

2022

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

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Structural inspection is essential to improve the safety and sustainability of infrastructure systems, such as bridges. Therefore, several technologies have been developed to detect defects automatically and accurately. For example, instead of using naked eye for bridge surface defect detection, which is subjective and risky, Light Detection and Ranging can collect high-quality 3D point clouds.This paperpresents the Surface Normal Enhanced PointNet++ (SNEPointNet++),which is a modified version of thewell-knownPointNet++methodapplied to thetask of concrete surface defectdetection. To this end, a point clouddatasetfrom three bridges in Montreal was collected, annotated,and classified into the three classesofcracks, spalls, and no-defects. Based on the comparison between the results(IoU)from the proposed method and similar researchdoneon the same dataset, there areat least 54% and 13% performance improvementsin detecting cracks and spalls,respectively.

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Section: Discussionmentioning

confidence: 97%

Point Cloud-Based Concrete Surface Defect Semantic Segmentation Using Modified PointNet++

Bolourian¹,

Hammad²,

Ghelman³

2022

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

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“…Their experimental results showed that the proposed method achieves better segmentation than U-Net and LinkNet. Fu et al 69 introduced Dense ASPP in the DeepLabv3 for segmenting bridge cracks. The proposed method achieved an mIoU of 82.37%, which is better than that of the original DeepLabv3.…”

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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“…Various image processing techniques are applied on the image to detect cracks, including morphological operations [ 99 ], wavelets [ 100 ], image binarization [ 97 , 101 ], seed growth [ 102 ], digital image correlation [ 103 – 107 ], and edge detectors [ 108 ]. Whereas, the robustness and adaptiveness of the traditional image treatment methods were quite limited once the imaging conditions (light changes, surface textures and so on) had been changed [ 109 ]. Nowadays, in medical image processing, artificial intelligence (especially deep learning) as one of the emerging technologies has brought great benefits for image-based crack detection [ 21 ].…”

Section: Ai-based Image Analysis For Crack Detectionmentioning

confidence: 99%

A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis

Guo

Chen

et al. 2022

BioMed Eng OnLine

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Despite numerous clinical trials and pre-clinical developments, the diagnosis of cracked tooth, especially in the early stages, remains a challenge. Cracked tooth syndrome is often accompanied by dramatic painful responses from occlusion and temperature stimulation, which has become one of the leading causes for tooth loss in adults. Current clinical diagnostical approaches for cracked tooth have been widely investigated based on X-rays, optical light, ultrasound wave, etc. Advances in artificial intelligence (AI) development have unlocked the possibility of detecting the crack in a more intellectual and automotive way. This may lead to the possibility of further enhancement of the diagnostic accuracy for cracked tooth disease. In this review, various medical imaging technologies for diagnosing cracked tooth are overviewed. In particular, the imaging modality, effect and the advantages of each diagnostic technique are discussed. What’s more, AI-based crack detection and classification methods, especially the convolutional neural network (CNN)-based algorithms, including image classification (AlexNet), object detection (YOLO, Faster-RCNN), semantic segmentation (U-Net, Segnet) are comprehensively reviewed. Finally, the future perspectives and challenges in the diagnosis of the cracked tooth are lighted.

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Bridge Crack Semantic Segmentation Based on Improved Deeplabv3+

Cited by 68 publications

References 38 publications

Point Cloud-Based Concrete Surface Defect Semantic Segmentation Using Modified PointNet++

Point Cloud-Based Concrete Surface Defect Semantic Segmentation Using Modified PointNet++

Review of artificial intelligence-based bridge damage detection

A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis

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