Spring Rolls' Size Inspection System Using Deep Image Processing

This conference paper presents a novel and efficient system for inspecting the size of spring rolls using advanced deep image processing techniques. With the increasing demand for high-quality food products in the food industry, ensuring the consistency and precision of dimensions in spring rolls is crucial for both manufacturers and consumers. Traditional inspection methods often fall short in accuracy and speed, motivating the exploration of innovative technologies such as deep learning. The proposed system leverages state-of-the-art deep neural networks to analyze high-resolution images of spring rolls captured during the manufacturing process. A convolutional neural network (CNN) architecture is employed to train on a diverse dataset of annotated spring roll images to accurately detect and measure the dimensions of individual rolls. The integration of deep image processing technologies not only improves the accuracy of size inspection but also contributes to increased production efficiency and reduced waste.