Enhancing Road Crack Localization for Sustainable Road Safety Using HCTNet

Dhirendra Prasad Yadav, Bhisham Sharma (), Shivank Chauhan, Farhan Amin () and Rashid Abbasi
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Dhirendra Prasad Yadav: Department of Computer Engineering & Applications, G.L.A. University, Mathura 281406, Uttar Pradesh, India
Bhisham Sharma: Centre of Research Impact and Outcome, Chitkara University, Rajpura 140401, Punjab, India
Shivank Chauhan: Department of Computer Engineering & Applications, G.L.A. University, Mathura 281406, Uttar Pradesh, India
Farhan Amin: School of Computer Science and Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
Rashid Abbasi: School of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China

Sustainability, 2024, vol. 16, issue 11, 1-21

Abstract: 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%.

Keywords: sustainable; road; crack; fusion; segmentation; CNN; vision transformer (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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