- Because road damages have resulted in numerous deaths, research into road damage detection, particularly hazardous road damage detection and warning, is essential for traffic safety.
- Existing road damage detection systems mostly process data on the cloud, which has a large latency due to long-distance transmission. Meanwhile, in these systems that require big, carefully labelled datasets to achieve outstanding performance, supervised machine learning methods are typically used.
- In this study, we suggest using Deep Learning to detect and warn about road damage. The foundation of road surface analysis is visual observations by persons and quantitative analysis by pricey tools
- CNN :
- Convolutional Neural Networks specialized for applications in image & video recognition. CNN is mainly used in image analysis tasks like Image recognition, Object detection & Segmentation.
- There are Four types of layers in Convolutional Neural Networks:
- CNN :
- Poor Accuracy while processing Light time Videos
- Results will vary depends upon the road color
YOLO Object Detection
- Yolo reframes object detection as a single regression problem. YOLO looks at the input image just once, and divides it into a grid of S x S cells. Each grid cell predicts B bounding boxes, a confidence score representing the intersection over union (IOU) with the ground truth bounding box, and the probability that the predicted bounding box contains some objects.
- We have written APIS for the addition, deletion, display and check functions. From UI, api calls are made to the backend and respective functions are made
- ►We will capture the video using VideoCapture object and after the capturing has been initialized every video frame is decoded (i.e. converting into a sequence of images).
- Front End – Anaconda IDE
- Backend – SQL
- Language – Python 3.8
- •Hard Disk: Greater than 500 GB
- •RAM: Greater than 4 GB
- •Processor: I3 and Above
- * Base Paper
- * Complete Source Code
- * Complete Documentation
- * Complete Presentation Slides
- * Flow Diagram
- * Database File
- * Screenshots
- * Execution Procedure
- * Readme File
- * Addons
- * Video Tutorials
- * Supporting Softwares
- * 24/7 Support * Ticketing System
- * Voice Conference
- * Video On Demand
- * Remote Connectivity
- * Code Customization
- * Document Customization
- * Live Chat Support