An automatic assessment of road condition from aerial imagery using modified VGG architecture in faster-RCNN framework

Article type: Research Article

Authors: Malini, A.^{; *} | Priyadharshini, P. | Sabeena, S.

Affiliations: Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, TamilNadu, India

Correspondence: [*] Corresponding author. A. Malini, Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai-625015, TamilNadu, India. E-mail: amcse@tce.edu.

Abstract: To develop a surveillance and detection system for automating the process of road maintenance work which is being carried out by surveying and inspection of roads manually in the current situation. The need of the system lies in the fact that traditional methods are time-consuming, tiresome and require huge workforce. This paper proposes an automation system using Unmanned Aerial Vehicle which monitors and detects the pavement defects like cracks and potholes by processing real-time video footage of Indian highways. The collected data is processed and stored as images in a road defects database which serves as input for the system. The behavior of Region Proposal Network (RPN) is made smooth by varying the number of region proposals utilized in the model. A regularization technique called dropout is used to achieve higher performance in the proposed Faster Region based Convolutional Neural Networks object detection model. The detections are made with 62.3% mean Average Precision @ Intersection over Union (IoU)> = 0.5 for the generation of 300 region proposals which is a good score for object detections. The comparisons between proposed and existing systems shows that the proposed Faster RCNN with modified VGG-16 performs well than the existing variants.

Keywords: pothole, region proposal network, mean average precision, unmanned aerial vehicle, dropout

DOI: 10.3233/JIFS-202596

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11411-11422, 2021