Zuhaib Saadat

Academic

Career

Verbatim text

Cloud-Vision Based Road Fault Detection 
Using Yololl And RTAB-Mapping 
This project aims to develop an advanced road fault detection system leveraging cloud-based 
computer vision and mapping techniques. The lack of automated road monitoring solutions leads to 
infrastructure deterioration, vehicle damage, and safety risks. Our system utilizes YOLOv11 for real-
time road defect detection and RTAB Mapping for precise spatial localization. The detected faults 
are processed and stored in the cloud, where they can be accessed through a dedicated web 
application. The web app visualizes road conditions using Azure Maps and provides real-time alerts, 
enabling authorities to monitor and respond efficiently. By integrating cloud computing, the system 
ensures scalability, remote accessibility, and real-time analytics to improve road maintenance and 
safety. 
Super vis or: Or. Muhammad Tariq 
Ablltract 
en automated road 
SLAM to detect e nd map 
YOLOll for object detection en 
reconstruction. the system capture 
LIOAR. GPS. IMU. a nd a camera mounted o 
collected date is processed end uploaded to Az 
Azure Digital Twins. enabling reel- time monitoring. v 
a nd automated a le rts for proactive maintena nce. 
I Block Diagram 
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Ope n CV 
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Azure 
Technology Used: 
Microsoft Azure, Ultralytics, Matlab, 
Raspberry Pi , ROS2 , Python , Visual Studio , 
Open CV 
Supervisor Name: 
Dr. Muhammad Tariq 
Group Members: 
Abdullah Naghman {21i-0905) 
Zuhaib Saadat {21i-0946) 
llqa Shahid (21i-0958) 
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