An automatic assessment of road condition from aerial imagery using modified VGG architecture in faster-RCNN framework
| Autor: | S. Sabeena, A. Malini, P. Priyadharshini |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Computer science business.industry 0211 other engineering and technologies General Engineering 02 engineering and technology Aerial imagery Artificial Intelligence 021105 building & construction 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Architecture business Road condition |
| Zdroj: | Journal of Intelligent & Fuzzy Systems. 40:11411-11422 |
| ISSN: | 1875-8967 1064-1246 |
| DOI: | 10.3233/jifs-202596 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|