Abstract
Roads are the major connection between towns, cities, states, and countries. Due to poor construction, heavy pouring, and very heavy-weight vehicles, often potholes are formed on the asphalt layer. These potholes are also one of the major causes of road accidents. The main aim of this project is to build an efficient as well as effective system for detecting potholes. In this regard, the image processing techniques are integrated with the convolutional neural network (CNN). Images of the potholes are taken from the video feed of the drone. The frames with pothole images are acquired and the features are extracted by the image processing which are then fed into the CNN model. The CNN model is trained and tested with 784 images which are labeled as normal and pothole. The model showed training and testing accuracy of 0.9902 and 0.9912, respectively. The strength of the proposed approach is its practical applicability and ability to detect potholes in real time. In order to test the effectiveness of the prototype and the proposed approach, it is tested for 25 different instances for identifying the potholes in roads. Finally, the time taken for detection of the potholes by the proposed approach is tested.
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Dalmia, D., Jain, R., Hussain, S.A.I. (2023). Intelligent System for Real-Time Potholes Monitoring and Detection. In: Pandit, M., Gaur, M.K., Kumar, S. (eds) Artificial Intelligence and Sustainable Computing. ICSISCET 2022. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-1431-9_32
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DOI: https://doi.org/10.1007/978-981-99-1431-9_32
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