ABSTRACT

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

EXISTING SYSTEM

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:

DISADVANTAGES

PROPOSED SYSTEM

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).

PROJECT VIDEO

Software Requirements:

Hardware Requirements

Including Packages

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Specialization =======================

5 Comments

AI CAR WITH REAL-TIME DETECTION OF DAMAGED ROAD AND LANE DETECTION