Quantitative Road Crack Evaluation by a U-Net Architecture using Smartphone Images and Lidar Data

Yamaguchi, Takahiro; Mizutani, Tsukasa

doi:10.36227/techrxiv.21172216

Cited by 5 publications

(4 citation statements)

References 33 publications

(39 reference statements)

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“…Pei et al 17 used the Cascade R-CNN model and various data augmentation techniques, achieving an F1 score of 0.635 in global road damage detection. Yamaguchi T et al 18 developed a method for accurately assessing road cracks using U-Net through LiDAR data enhancement and morphological transformation. Arya et al 19 used the lightweight network MobileNet to detect road damage images from the RDD2020 datasets, achieving an F1 score of 0.52.…”

Section: Introductionmentioning

confidence: 99%

YOLOv8-PD:An Improved Road damage detection algorithm based on YOLOv8n model

zeng,

Zhong

2024

Preprint

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Road damage detection is an crucial task to ensure road safety. To tackle the issues of poor performance on multi-scale cracks and high costs in road damage detection, this paper presents an improved lightweight road damage detection algorithm based on YOLOv8n, named YOLOv8-PD (Pavement Disease). Firstly, a BOT module that can extract global information of road damage images is proposed to adapt to the large-span features of crack objects. Secondly, the introduction of the Large Separable Kernel Attention (LKSA) mechanism enhances the detection accuracy of the algorithm. Then, a C2fGhost block is constructed in the neck network to strengthen the feature extraction of complex road diseases while reducing the computational load. Furthermore, we introduced Lightweight Shared Convolution Detection Head (LSCD-Head) to improve feature expressiveness and reduce the number of parameters. Finally, extensive experiments on the RDD2022 dataset yield a model with parametric and computational quantities of 2.3M and 6.1 GFLOPs, which are only 74.1% and 74.3% of the baseline, and the mAP reaches an improvement of 1.4 percentage points from the baseline. In addition, experimental results on the RoadDamage dataset show that the mAP increased by 4.2% and this algorithm has good robustness. This method can provide a reference for the automatic detection method of pavement cracks.

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Section: Introductionmentioning

confidence: 99%

YOLOv8-PD:An Improved Road damage detection algorithm based on YOLOv8n model

zeng,

Zhong

2024

Preprint

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“…(2019) pruned and utilized VGG16 to classify corrosion and crack defects in bridge structures, achieving accuracy rates of 93.6% and 98.5%, respectively. Novel networks like Faster Region‐CNN (Faster RCNN) and You Only Look Once (YOLO) series (Chun et al., 2023; Z. Zhou et al., 2022) have emerged for object detection, while Mask RCNN and U‐Net (Yamaguchi & Mizutani, 2023) are employed for segmentation. Recent researchers proposed EfficientNet and incorporated attention mechanisms (Chen & He, 2022; Y. Pan & Zhang, 2022; L. Zhang et al., 2023), separable convolution (Zhu et al., 2023; Zou et al., 2022), deformable convolution (Lei et al., 2023), atrous convolution (Siriborvornratanakul, 2023), and other strategies (Zheng et al., 2022) to further enhance model performance.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang,

Shen,

Lin

et al. 2023

Computer aided Civil Eng

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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.

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“…Previous crack detection algorithms are categorized into (1) image processing, (2) machine learning (ML), and (3) deep learning (DL)‐based algorithms (e.g., Shi et al., 2016; Yamaguchi & Mizutani, 2023; Yang et al., 2020). Considering target tasks, the algorithms can be also divided into (a) classification, (b) localization, and (c) segmentation‐type algorithms.…”

Section: Introductionmentioning

confidence: 99%

Road crack detection interpreting background images by convolutional neural networks and a self‐organizing map

Yamaguchi,

Mizutani

2023

Computer aided Civil Eng

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The presence of road cracks is an important indicator of damage. Deep learning is a prevailing method for detecting cracks in road surface images because of its detection ability. Previous research works focused on supervised convolutional neural networks (CNNs) without non‐crack features or unsupervised crack analysis with limited accuracies. The novelty of this study is the addition of background classification. By increasing the number of non‐crack categories, CNNs are driven to learn non‐crack features and improve crack detection performances. Non‐crack images are preprocessed, and their features are extracted in an unsupervised way by a deep convolutional autoencoder. A self‐organizing map clusters features to obtain non‐crack categories. This study focusses on classification though the method can be adopted in parallel with the latest segmentation algorithms. Using common road crack datasets, modified deep CNN models significantly improved accuracy by 1%–4% and f‐measure by 3%–8%, compared to previous models. The modified visual geometry group (VGG) 16 showed the top‐level performance, 96% accuracy and 84%–85% f‐measure. The models drastically reduced false detection cases while maintaining their crack detection abilities.

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Quantitative Road Crack Evaluation by a U-Net Architecture using Smartphone Images and Lidar Data

Cited by 5 publications

References 33 publications

YOLOv8-PD:An Improved Road damage detection algorithm based on YOLOv8n model

YOLOv8-PD:An Improved Road damage detection algorithm based on YOLOv8n model

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

Road crack detection interpreting background images by convolutional neural networks and a self‐organizing map

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