TY - CONF
T1 - Introduction to Robotics Remote/Voice Controlled Car
AU - Rababaah, Aaron
AU - Meulien, Alexandre
AU - Al-Abdulsalam, Ibrahim
AU - AlTurkait, Farah
N1 - Rababaah, A., Ahmed Hassan, Alexandre Thorgal Meulien, IbrahimAbdulsalam, Farah AlTurkait, Tarek Dafar (5-6 Aug, 2022). Introduction to Robotics Remote/VoiceControlled Car. 2022 USTM-AIMT SUMMER International Conference
PY - 2022
Y1 - 2022
N2 - This paper presents an investigation of the effectiveness of a Machine Deep Learning (DL) model using Convolution Neural Networks (CNN) in detecting cracks in asphalt pavement. Pavement cracks have been studied for decades using traditional Machine Learning (ML) methods such as neural networks, genetic algorithms, fuzzy logic, etc. In this work, we present a DL model based on CNN to study the effectiveness of modern machine vision methods on an old problem of pavement crack detection. We provide our methodology in developing the proposed model and the validation process in this paper. A dataset consisting of 500 sample images was used to test the model and our experiments showed that the proposed model is effective with a mean accuracy of 96% and a standard deviation of. 025. Future work is recommended to be on crack type classification after the successful detection process.
AB - This paper presents an investigation of the effectiveness of a Machine Deep Learning (DL) model using Convolution Neural Networks (CNN) in detecting cracks in asphalt pavement. Pavement cracks have been studied for decades using traditional Machine Learning (ML) methods such as neural networks, genetic algorithms, fuzzy logic, etc. In this work, we present a DL model based on CNN to study the effectiveness of modern machine vision methods on an old problem of pavement crack detection. We provide our methodology in developing the proposed model and the validation process in this paper. A dataset consisting of 500 sample images was used to test the model and our experiments showed that the proposed model is effective with a mean accuracy of 96% and a standard deviation of. 025. Future work is recommended to be on crack type classification after the successful detection process.
M3 - Paper
T2 - AIMT International Conference
Y2 - 1 January 2022 through 1 January 2022
ER -