Intelligent road quantification system (IRQS)
| dc.contributor.author | Uzair Farid Khan Saddozai | |
| dc.contributor.author | Syed Muhammad Asad Raza Kazmi | |
| dc.contributor.author | Salman Tauheed Bhatti | |
| dc.contributor.author | Muhammad Fasih Tariq | |
| dc.contributor.author | Syed Habeeb Haider Zaidi | |
| dc.date.accessioned | 2025-12-26T19:28:26Z | |
| dc.date.available | 2025-12-26T19:28:26Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | The idea for this project is to build an improved road quantification system that tackles the important problem of road surface degradation, primarily concerning the identification and prognosis of potholes and cracks. The goal is to design something that can effectively reach its target of improving the road maintenance task using the assistance of computer vision and machine learning methods. The practical aspect of the study includes the use of imagery from the road surfaces using a dedicated camera that is mounted on a vehicle. The data collected here passes through some preprocessing where the targets, such as pedestrians, vehicles, pavement and others of similar nature are first removed as impurities. The core of the system integrates several key technologies: U-Net is applied in the first step the segmentation, and it is needed to find the geometry and positions of the road. YOLO is then used for detection and gives out the bounding boxes and class probabilities. To increase the accuracy of predictions within successive frames, the use of HMM (Gaussian Mixture Model Hidden Markov Model) is used for the prediction of potholes and cracks. YOLO is re-applied occasionally in order to check and update these predictions so as to maintain perpetuity and efficiency of the detection process. Such, the results indicate that the usage of the described integrated approach enhances the efficiency of the detected road defects and their predictions, making it easier to perform relevant maintenance works as soon as possible. As mentioned earlier, the extracted data is saved in the local storage of the chosen device. Therefore, the workflow of the project allows using the detection, segmentation, and prediction of the road situation to create an effective road monitoring system. This solution deals with one of the most critical and compelling issues of modern society, namely the lack of proper and timely roads maintenance, which improves the safety of roads and their management. | |
| dc.identifier.uri | https://escholar.umt.edu.pk/handle/123456789/18063 | |
| dc.language.iso | en | |
| dc.publisher | UMT, Lahore | |
| dc.title | Intelligent road quantification system (IRQS) | |
| dc.type | Thesis |