Abstract
Bad road conditions, such as cracks and potholes, can cause passenger discomfort, vehicle damage, and accidents. Condition of roads indirectly effects on growth of the country. Hence, there is a need for such a system that can detect potholes. It would allow vehicles to issue alerts to identify potholes so that drivers can reduce the speed and avoid them and make the ride smooth. Many researchers had developed various algorithms to become aware of potholes on roads. In this paper, the proposed system detects the potholes using You Only Look Once version 2(YOLOv2) and a convolutional neural network (CNN). The predefined CNN, namely resnet50, is used to extract the features of testing images and training images. Kaggle data set is used to evaluate the proposed algorithm. The experimental results are evaluated in terms of precision rate and recall rate. The proposed approach precision rate is 94.04% for test images.
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Sumalatha, R., Rao, R.V., Devi, S.M.R. (2022). Pothole Detection Using YOLOv2 Object Detection Network and Convolutional Neural Network. In: Iyer, B., Ghosh, D., Balas, V.E. (eds) Applied Information Processing Systems . Advances in Intelligent Systems and Computing, vol 1354. Springer, Singapore. https://doi.org/10.1007/978-981-16-2008-9_28
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DOI: https://doi.org/10.1007/978-981-16-2008-9_28
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