Automatic Prediction of Road Angles using Deep Learning-Based Transfer Learning Models. Issue 1 (March 2021)

• Record Type: Journal Article
• Authors: Sharma, Sparsh; Jhaketiya, Vinit; Kaul, Ajay; Raza, Abrar Ahmed; Ahmed, Suhaib; Naseem, Mohd.
• Is Part Of: IOP conference series. Volume 1099:Issue 1(2021)
• Publication Date: 2021-03
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1099/1/012060
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: The construction of robust and complex roads requires a considerable amount of resources, time, and effort. These efforts get manifold, especially when a road is to be constructed on rugged terrains. The use of Artificial Intelligence (AI) can be seen in almost all the research sectors. The efforts and the cost involved in road construction can be reduced by the induction of AI-based techniques for the estimation of road construction-related parameters that are required to predict the tentative cost incurred in the whole road construction project. In this article, the angle, which is one of the crucial parameters that aids in estimating the total cost and time required for the whole road construction project is predicted using the variation of three transfer learning-based deep learning models viz. VGG-16, DenseNet-121, and DenseNet-169. The proposed VGG16 based CNN model performance is computed and compared in terms of the evaluation metrics like Mean Square Estimation (MSE), Mean Absolute Error (MAE), and Correlation Coefficient (R 2 ). Based upon the simulation results conducted, it was observed that VGG-16 has yielded road angles with less difference error.