We present a low-cost, camera-based tactile sensor that leverages the photoelastic effect—interference fringes that appear under stress—to estimate contact force, position, and shape. Each fringe image is recorded at 50 Hz and processed by a multi-task neural network that predicts (i) the normal force (Fz ), (ii) the 2D contact location (x, y), and (iii) the shape class of the object. Two sensor variants were developed: Sensor 1, a layered design with fewer visible fringes, and Sensor 2, an integrated structure with improved fringe clarity. Both were evaluated using a ResNet-18 and a lightweight custom CNN, under three augmentation pipelines: grayscale images with 10 noisy augmented samples each, RGB images with 3 noisy augmentations, and RGB images with 3 clean (noise-free) augmentations. The base dataset includes nearly 15,000 synchronised samples of high-frequency fringe images and force signals. With augmentation, this was expanded to around 45,000 or 150,000 samples depending on the pipeline. The best results were achieved using Sensor 1 and ResNet-18 trained on grayscale images with 10 augmentations per input image. This configuration yielded a force MSE of 0.0213 N2, a contactpoint RMSE of 0.4462 mm, and 96.24% shape classification accuracy. Notably, even RGB images with only three augmentations per sample reached similar performance levels. These findings highlight that full-colour input and lightweight augmentation remain effective for accurate, scalable tactile sensing. Our modular learning pipeline generalises across sensor variants and data regimes, enabling robust, highfrequency tactile inference suitable for real-world deployment.

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.