Modeling automatic pavement crack object detection and pixel-level segmentation

Du, Yuchuan; Zhong, Shan; Fang, Hongyuan; Wang, Niannian; Liu, Chenglong; Wu, Difei; Sun, Yan; Xiang, Mang

doi:10.1016/j.autcon.2023.104840

Cited by 29 publications

(3 citation statements)

References 35 publications

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“…Du et al [79]. proposed an efficient road crack detection method that can perform object detection and semantic segmentation simultaneously.…”

Section: Detectionmentioning

confidence: 99%

How to Make a State of the Art Report—Case Study—Image-Based Road Crack Detection: A Scientometric Literature Review

Fan,

Tang,

Mohd Ariffin

et al. 2024

Applied Sciences

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With the rapid growth in urban construction in Malaysia, road breakage has challenged traditional manual inspection methods. In order to quickly and accurately detect the extent of road breakage, it is crucial to apply automated road crack detection techniques. Researchers have long studied image-based road crack detection techniques, especially the deep learning methods that have emerged in recent years, leading to breakthrough developments in the field. However, many issues remain in road crack detection methods using deep learning techniques. The field lacks state-of-the-art systematic reviews that can scientifically and effectively analyze existing works, document research trends, summarize outstanding research results, and identify remaining shortcomings. To conduct a systematic review of the relevant literature, a bibliometric analysis and a critical analysis of the papers published in the field were performed. VOSviewer and CiteSpace text mining tools were used to analyze and visualize the bibliometric analysis of some parameters derived from the articles. The history and current status of research in the field by authors from all over the world are elucidated and future trends are analyzed.

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“…Du et al [79]. proposed an efficient road crack detection method that can perform object detection and semantic segmentation simultaneously.…”

Section: Detectionmentioning

confidence: 99%

How to Make a State of the Art Report—Case Study—Image-Based Road Crack Detection: A Scientometric Literature Review

Fan,

Tang,

Mohd Ariffin

et al. 2024

Applied Sciences

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“…Chen et al [ 66 ] have demonstrated impressive recognition accuracy in identifying different types of cracks by incorporating the Convolutional Block Attention Module (CBAM) into MobileNetV3 as the backbone network. Du et al [ 67 ] have proposed an Attention Feature Pyramid Network that enhances the precise segmentation of road cracks within the YOLOv4 model. Similarly, Yang et al [ 68 ] introduced a multi-scale, tri-attention network, termed MST-NET.…”

Section: Introductionmentioning

confidence: 99%

A Binocular Vision-Based Crack Detection and Measurement Method Incorporating Semantic Segmentation

Zhang,

Shen,

Liu

et al. 2023

Sensors

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The morphological characteristics of a crack serve as crucial indicators for rating the condition of the concrete bridge components. Previous studies have predominantly employed deep learning techniques for pixel-level crack detection, while occasionally incorporating monocular devices to quantify the crack dimensions. However, the practical implementation of such methods with the assistance of robots or unmanned aerial vehicles (UAVs) is severely hindered due to their restrictions in frontal image acquisition at known distances. To explore a non-contact inspection approach with enhanced flexibility, efficiency and accuracy, a binocular stereo vision-based method incorporating full convolutional network (FCN) is proposed for detecting and measuring cracks. Firstly, our FCN leverages the benefits of the encoder–decoder architecture to enable precise crack segmentation while simultaneously emphasizing edge details at a rate of approximately four pictures per second in a database that is dominated by complex background cracks. The training results demonstrate a precision of 83.85%, a recall of 85.74% and an F1 score of 84.14%. Secondly, the utilization of binocular stereo vision improves the shooting flexibility and streamlines the image acquisition process. Furthermore, the introduction of a central projection scheme achieves reliable three-dimensional (3D) reconstruction of the crack morphology, effectively avoiding mismatches between the two views and providing more comprehensive dimensional depiction for cracks. An experimental test is also conducted on cracked concrete specimens, where the relative measurement error in crack width ranges from −3.9% to 36.0%, indicating the practical feasibility of our proposed method.

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“…Segmentation models delineate the defect's boundaries at the pixel level [12]. Object detection algorithms encircle the defect with a bounding box and provide distress label [13]. In the following, the current state-of-the-art in computer-aided road damage detection is comprehensively reviewed to thoroughly understand the prevailing limitations, which are subsequently addressed in this paper.…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Computer Vision System Based on Deep Learning for Pavement Distress Detection and Quantification

Cano-Ortiz

Iglesias

Arbol

et al. 2023

Preprint

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Modeling automatic pavement crack object detection and pixel-level segmentation

Cited by 29 publications

References 35 publications

How to Make a State of the Art Report—Case Study—Image-Based Road Crack Detection: A Scientometric Literature Review

How to Make a State of the Art Report—Case Study—Image-Based Road Crack Detection: A Scientometric Literature Review

A Binocular Vision-Based Crack Detection and Measurement Method Incorporating Semantic Segmentation

An End-to-End Computer Vision System Based on Deep Learning for Pavement Distress Detection and Quantification

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