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

• the existing system, Convolutional Neural Networks (CNNs) are used for detecting lanes and potholes on roads. CNN is a deep learning technique that excels in image classification and feature extraction, making it a popular choice for vision-based applications. The CNN-based approach processes road images, extracts lane boundaries, and identifies potholes by learning spatial patterns from the dataset.

  For lane detection, the CNN model identifies lane markings based on pixel intensity and edge detection. It is trained on a dataset containing road images with labeled lane markings. The model segments lane areas and classifies road sections as lane or non-lane regions. However, this method struggles when lane markings are faded, occluded, or affected by lighting conditions.

  For pothole detection, the CNN model detects irregularities in the road surface based on texture and depth variations. It learns features such as cracks, depressions, and potholes from training images. However, CNN relies heavily on high-quality labeled data and may not generalize well to real-world conditions with different road textures, lighting variations, and environmental factors like rain and fog.

DISADVANTAGES

• Slow Processing Speed – CNN requires multiple layers of feature extraction, making real-time detection challenging.
• Limited Generalization – The model struggles to detect lanes and potholes in complex environments with occlusions, shadows, or low-visibility conditions.
• High Computational Requirements – CNN-based models demand significant GPU resources for training and real-time execution.
• Difficulty in Detecting Curved and Faded Lanes – Traditional CNN-based methods fail to track lanes accurately in curved roads or areas with worn-out lane markings.
• Inaccurate Pothole Classification – CNN may misclassify road anomalies, such as oil spills or road patches, as potholes due to its dependency on texture-based feature learning.

PROPOSED SYSTEM

To overcome the limitations of CNN-based detection, the proposed system utilizes YOLO (You Only Look Once), a real-time object detection model, for lane and pothole detection. Unlike CNN, which processes images sequentially, YOLO detects objects in a single forward pass, making it significantly faster and more efficient for autonomous driving applications.

For lane detection, YOLO treats lane markings as objects and detects them using bounding boxes. The model is trained on annotated road datasets, allowing it to recognize lanes even in challenging conditions such as night driving, rain, and curved roads. By leveraging YOLO’s grid-based object detection, the system can track lanes more reliably and ensure the vehicle stays within safe driving boundaries.

For pothole detection, YOLO’s feature extraction and classification capabilities enable it to accurately differentiate potholes from cracks, road patches, and water puddles. The system detects potholes in real-time and provides alerts to drivers or autonomous vehicles, reducing accident risks.

PROJECT VIDEO

Software Requirements:

• Front End – Anaconda IDE
• Backend – SQL
• Language – Python 3.8

Hardware Requirements

• •Hard Disk: Greater than 500 GB
• •RAM: Greater than 4 GB
• •Processor: I3 and Above

Including Packages

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• * Base Paper
• * Complete Source Code
• * Complete Documentation
• * Complete Presentation Slides
• * Flow Diagram
• * Database File
• * Screenshots
• * Execution Procedure
• * Readme File
• * Addons
• * Video Tutorials
• * Supporting Softwares

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

• * 24/7 Support * Ticketing System
• * Voice Conference
• * Video On Demand 
• * Remote Connectivity
• * Code Customization
• * Document Customization 
• * Live Chat Support