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.
Image restoration is the method of restoring an image to its original state by removing noise and blur. Image disclarity is crucial to maintain in a variety of cases, including photography, where motion blur is caused by camera shake when taking images, radar imaging, where the impact of image system reaction is removed, and so on. Image noise is an unwanted signal that appears in an image from a sensor, such as a power / energy signal, or from the atmosphere, such as rain or snow. Coding artefacts, resolution limitations, transmission noise, object motion, camera shake, or a confluence of events could cause image degradation. With the intention of separating HF and LF objects, image decomposition is used to decompose the distorted image into a pattern layer (High Frequency Component) and a framework layer (Low Frequency Component).
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