Overall, the proposed approach in this thesis offers a cost-effective, accessible, and accurate system for pavement distress detection, with the integration of fast perspective transformation and spatial data analysis playing a pivotal role in improving the assessment and management of road infrastructure. The structure of this thesis is organized as follows: • Introduction: This section sets the stage for the research by highlighting the importance of pavement distress detection, the challenges of traditional methods, and the contributions of the thesis. • Literature Review: This section provides a comprehensive overview of the existing literature, covering both background of general models in the field of computer vision and specific techniques in pavement distress detection, or anything directly related to the thesis: ‣ Background – Traditional Automated Techniques: Discussion on sensor-based solutions, stereo vision methods, and detection with structured light. – Deep Learning in Computer Vision: Examination of the evolution of deep learning models in computer vision, as well as important architectures such as the U-Net, the R-CNN, the YOLO model family, and latest transformer-based networks. – Depth Estimation Methods: Exploration of stereo vision and disparity maps, monocular depth estimation, and the integration of depth perception with an example. – Geographic Information Systems (GIS) Integration: Analysis of how GIS can be integrated into pavement distress detection. ‣ Related Work: Review of recent studies and research papers in the field of pavement distress detection, focusing on the methodologies, techniques, and findings. – Deep Learning based Distress Detection: include transfer learning and previous work on neural network based pavement distress detection models. • Data Collection, Cleansing, Labeling, and Transformation: This section describes the methodology and equipment selection, transportation selection, data collection strategy, data cleansing, data labeling, and data transformation processes. • Model Re-implementation and Tweaking: It focuses on model selection and re-implementation (Mask R-CNN), as well as model tweaking and hyperparameter optimization. • GIS Analysis for Pavement Distress Detection: This section discusses the integration of spatial data analysis using Geographic Information Systems (GIS) to provide geographic context to the pavement distress data. • Conclusion and Future Work: Providing the key findings of the thesis and outlines potential directions for future research.