Abstract
Research on damage detection of road surfaces has been an active area of research, but most studies have focused so far on the detection of the presence of damages. However, in real-world scenarios, road managers need to clearly understand the type of damage and its extent in order to take effective action in advance or to allocate the necessary resources. Moreover, currently there are few uniform and openly available road damage datasets, leading to a lack of a common benchmark for road damage detection. Such dataset could be used in a great variety of applications; herein, it is intended to serve as the acquisition component of a physical asset management tool which can aid governments agencies for planning purposes, or by infrastructure maintenance companies. In this paper, we make two contributions to address these issues. First, we present a large-scale road damage dataset, which includes a more balanced and representative set of damages. This dataset is composed of 18,034 road damage images captured with a smartphone, with 45,435 instances road surface damages. Second, we trained different types of object detection methods, both traditional (an LBP-cascaded classifier) and deep learning-based, specifically, MobileNet and RetinaNet, which are amenable for embedded and mobile and We compare the accuracy and inference time of all these models with others in the state of the art.
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Acknowledgements
We thank Prof. Benedetto from University de Roma Tre (Italy) for kindly providing some of the images used to complement the dataset from the University of Tokyo (Japan), along other images obtained by our group in Guadalajara, Mexico. We also thank the people of Vidrona LTD (Edinburgh, UK) for helping us to delimit the problem addressed in this paper, especially to Shailendra and Ashutosh Natraj.
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Angulo, A., Vega-Fernández, J.A., Aguilar-Lobo, L.M., Natraj, S., Ochoa-Ruiz, G. (2019). Road Damage Detection Acquisition System Based on Deep Neural Networks for Physical Asset Management. In: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_1
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