| dc.contributor.author | Nadeem, S.M Sheeraz Reg # 67700 | |
| dc.contributor.author | Hussain, S.M Khizar Reg # 65255 | |
| dc.contributor.author | Affan, Muhammad Reg # 67701 | |
| dc.date.accessioned | 2026-07-09T07:38:42Z | |
| dc.date.available | 2026-07-09T07:38:42Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/21425 | |
| dc.description | Supervised by Dr. Sameena Javaid | en_US |
| dc.description.abstract | In busy city traffic, it's super important to quickly spot emergency vehicles like ambulances. That’s what our project is all about - making traffic lights smarter by detecting ambulances in real-time. We want to create a clever traffic signal system that can see when an ambulance needs to get through. When that happens, the signal changes from red to green so the ambulance can keep going without any delays on its way to the hospital. To make this happen, we went out on the streets of Karachi and collected data about all kinds of ambulances. This big set of information is the core of our project, helping us train and check how well our system can spot ambulances. We made an important choice to switch from YOLOv5 to YOLOv8, a move we carefully thought about. YOLOv8 has some cool improvements that match what we need, making our system really good at spotting ambulances accurately. Our project is not just about technology; it's like a journey into the world of computers and learning machines. All our hard work paid off, and we now have results showing our system is more than 90% accurate in spotting ambulances during practice and testing. But our goal is not just numbers - we want to make things easy for people to understand. So, the next step for us is to create a simple interface that shows how our system works in real life. This way, people can see for themselves how our system spots ambulances in videos, proving that our traffic signal idea is practical and useful. In the end, our project is a mix of technology, new ideas, and making city traffic better. We're using the latest computer tricks to help traffic lights work smarter, especially in emergencies. Switching to YOLOv8, gathering data in Karachi, and focusing on making things simple for everyone are all part of our commitment to improve how traffic is managed. | en_US |
| dc.language.iso | en_US | en_US |
| dc.publisher | Bahria University Karachi Campus | en_US |
| dc.relation.ispartofseries | BSCS;MFN BSCS 496 | |
| dc.title | AUTOMATED TRAFFIC CONTROL SYSTEM FOR AMBULANCE | en_US |
| dc.type | Project Reports | en_US |