Abstract
The detection of road anomalies in highway management is a crucial yet manpower-demanding task. Although there have been many previous types of research based on visual and acceleration data collected from vehicles. However, to distinctively differentiate a pothole and speedbump from acceleration remains a challenge. The visual methods require high computation supported by expensive GPUs. This paper proposes a novel contribution to depth detection using angular velocity for pothole detection. The solution is accorded by three-layer architecture inclusive of fog-based cloud infrastructure where each layer performs a respective function based on computing capability. The data acquisition is accomplished using an IoV setup equipped with M8N GPS, and MPU-6050 mounted on an Arduino MEGA 2560. The results are compared with the state-of-art algorithms and methodologies. The proposed SVM-nAVDD achieved the precision of 0.9092–0.9123 for the tested datasets and depth/height detection with 92% accuracy.
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Kumar, N., Sood, S. & Saini, M. IoV-fog-cloud framework for road anomalies detection using SVM-nAVDD approach. J Ambient Intell Human Comput 14, 10899–10915 (2023). https://doi.org/10.1007/s12652-022-04358-y
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DOI: https://doi.org/10.1007/s12652-022-04358-y