Abstract
Road serves an irreplaceable tool of transportation. Yet, it is the house of tremendous accidents. The common distresses that can affect the performance of road surface are crack and damage. We developed a new dataset which consists of 3,533 road images were collected from Inner Ring Road, Chennai, Tamil Nadu, India. The main contribution of this paper deals to detect crack and damage on road surface and also classify types of cracks based on its structure and then identify the location and send the notification to the nearest road maintenance government department officials. Our proposed work deals to design a pre-trained ResNet-152 model by incorporating with faster region-based convolutional neural network (Faster R-CNN). We apply our proposed model to other existing benchmark datasets. Experimental analysis and results of our model were measured using precision, recall and F-measure.
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Aravindkumar, S., Varalakshmi, P., Alagappan, C. (2022). Automatic Road Surface Crack Detection Using Deep Learning Techniques. In: Raje, R.R., Hussain, F., Kannan, R.J. (eds) Artificial Intelligence and Technologies. Lecture Notes in Electrical Engineering, vol 806. Springer, Singapore. https://doi.org/10.1007/978-981-16-6448-9_4
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DOI: https://doi.org/10.1007/978-981-16-6448-9_4
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