Automated Detection for Concrete Surface Cracks Based on Deeplabv3+ BDF

Yu-jie, Shen; Yu, Zhenwei; Li, Chunsheng; Zhao, Chao; Sun, Zhilin

doi:10.3390/buildings13010118

Cited by 10 publications

(2 citation statements)

References 39 publications

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“…However, the accuracy of the model deteriorated when tested with unseen data. In [ 17 ] an optimized Deeplabv3+ BDF network is proposed for concrete crack segmentation. The network is trained using transfer learning, coarse-annotation, and fine-annotation techniques.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Approach for Robust Automated Crack Detection and Segmentation in Concrete Structures

Sohaib,

Jamil,

Kim

2024

Sensors

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To prevent potential instability the early detection of cracks is imperative due to the prevalent use of concrete in critical infrastructure. Automated techniques leveraging artificial intelligence, machine learning, and deep learning as the traditional manual inspection methods are time-consuming. The existing automated concrete crack detection algorithms, despite recent advancements, face challenges in robustness, particularly in precise crack detection amidst complex backgrounds and visual distractions, while also maintaining low inference times. Therefore, this paper introduces a novel ensemble mechanism based on multiple quantized You Only Look Once version 8 (YOLOv8) models for the detection and segmentation of cracks in concrete structures. The proposed model is tested on different concrete crack datasets yielding enhanced segmentation results with at least 89.62% precision and intersection over a union score of 0.88. Moreover, the inference time per image is reduced to 27 milliseconds which is at least a 5% improvement over other models in the comparison. This is achieved by amalgamating the predictions of the trained models to calculate the final segmentation mask. The noteworthy contributions of this work encompass the creation of a model with low inference time, an ensemble mechanism for robust crack segmentation, and the enhancement of the learning capabilities of crack detection models. The fast inference time of the model renders it appropriate for real-time applications, effectively tackling challenges in infrastructure maintenance and safety.

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Section: Introductionmentioning

confidence: 99%

An Ensemble Approach for Robust Automated Crack Detection and Segmentation in Concrete Structures

Sohaib,

Jamil,

Kim

2024

Sensors

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“…CNNs can obtain hierarchical representations from large datasets to capture complex image patterns and features [16]. Several variations of CNNs, such as U-Net, Fully Convolutional Networks (FCN), and DeepLabv3+, have been proposed for crack segmentation and have shown promising results [17][18][19][20][21][22]. In most cases, the categorization of small image patches could not reach accuracy at the pixel level, while reaching accuracy at the pixel level during searching for cracks.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Road Crack Localization for Sustainable Road Safety Using HCTNet

Yadav,

Sharma,

Chauhan

et al. 2024

Sustainability

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Road crack detection is crucial for maintaining and inspecting civil infrastructure, as cracks can pose a potential risk for sustainable road safety. Traditional methods for pavement crack detection are labour-intensive and time-consuming. In recent years, computer vision approaches have shown encouraging results in automating crack localization. However, the classical convolutional neural network (CNN)-based approach lacks global attention to the spatial features. To improve the crack localization in the road, we designed a vision transformer (ViT) and convolutional neural networks (CNNs)-based encoder and decoder. In addition, a gated-attention module in the decoder is designed to focus on the upsampling process. Furthermore, we proposed a hybrid loss function using binary cross-entropy and Dice loss to evaluate the model’s effectiveness. Our method achieved a recall, F1-score, and IoU of 98.54%, 98.07%, and 98.72% and 98.27%, 98.69%, and 98.76% on the Crack500 and Crack datasets, respectively. Meanwhile, on the proposed dataset, these figures were 96.89%, 97.20%, and 97.36%.

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