Crack Detection of Structures using Deep Learning Framework
Arunish Kumar, Amit Kumar, Avinash Kumar Jha, Ashutosh Trivedi
Abstract
The transportation system has drawn attention due to the growing threats of cracks to the roads and bridges. To accurately detect cracks was made possible by the introduction of deep learning. In this paper, we propose the modified LeNet 5 model for detecting cracks in roads and bridges. We summarize our work in the following points: - 1. We worked on the three datasets, i.e., Automated Bridge Crack Detection Dataset, Concrete Crack Images for Classification Dataset, and Asphalt Crack Dataset. 2. We applied a modified version of LeNet-5 model on Automated Bridge Crack Detection Dataset, Concrete Crack Images for Classification Dataset, and Asphalt Crack Dataset. 3. Then, we analyzed our results and compared the same with Principal Component Analysis and without using Principal Component Analysis. We are also highlighting the region of crack and non-crack using green and red color, respectively. Our proposed model takes both the factors of time and accuracy into consideration while producing the output.