This thesis presents a comprehensive analysis and implementation of a program designed to detect the number of steps leading up to the front door of a house using a Google Street View Image. The purpose of counting the steps is to have a proxy of the ground floor elevation by multiplying with an average step height. After determining the street level’s elevation from sea level, it becomes possible to assess the vulnerability of houses to flooding. Additionally, the system developed in this thesis provides a method for enhancing the effective resolution of computer vision models to be able to detect details with more accuracy, this is done through Region of Interest detection and enlargement.
The design process consists of four main stages: Acquiring and labelling data consisting of street view images which contain houses, staircases, and steps. Developing an initial model to gauge the ability of the current technology available. Developing algorithms to detect objects of interest in the images. Use a top-level to combine these algorithms and crop out any information that is not of interest.
In the structure of the final product, a top-level, that allowed a model to select a region of interest for step-detection and then cropped the image to this region of interest, was used. This approach allowed for an effective enhancement in resolution as the model is allowed to only focus on the ’useful’ information. The detection in these models was done by YOLOv8x-algorithm that was transfer learned on the custom dataset.
The final product had a precision of 98.7% in detecting steps, an area under the curve (AUC) of 98% in the PR-curve for steps and a deviation no larger than 1 step.