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.

The accurate prediction of object softness is crucial in many fields, from agriculture to medical care. Vision-based tactile sensors, which capture high-resolution images of contact interactions, have shown great potential in determining this material property. Many existing approaches, particularly those using end-to-end models, suffer from a 'black-box' problem where it is difficult to understand which features the models use to make their predictions. This lack of transparency makes it challenging to determine and correct errors. To overcome this, this paper shows a data-driven method that can decode information from acquired tactile images to extract pressure distributions and assign softness levels to different objects. A novel approach is explored, integrating a Convolutional Neural Network (CNN) to predict the pressure distribution and a Long Short-Term Memory (LSTM) network to assess material softness. It is demonstrated that the CNN model effectively learns necessary features from the tactile images, enabling precise pressure distribution predictions. Concurrently, the LSTM model analyzes temporal sequences of tactile data, accurately predicting material softness and differentiating ripe from overripe fruits. By utilizing the spatiotemporal pressure distribution, this method improves on existing methods by enabling the efficient use of tactile data and providing additional information that can be used to further enhance the model. This paper can be used as a stepping stone to a more complex system in which robotic control can be implemented based on the sensed material properties, allowing for better control loop mechanisms and expanding the applications of tactile sensing technologies.