Robust TRISO-Fueled Pebble Identification by Digit Recognition

Roshan Kenia, Jihane Mendil, Ahmed Jasim, Muthanna Al-Dahhan, Zhaozheng Yin; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 8157-8165

Nuclear power plays a vital role in providing reliable and clean energy to fulfill increasing demands in electricity worldwide. It continues to be an essential source of national power supply as growing concerns about fossil fuel depletion, global warming, and emissions require utilizing sustainable energy sources. One area contributing to the growth of nuclear power is the development of reactors that have enhanced protection and security, thermal efficiency, and design. Reactor efficiency can be studied by the burnup that occurs when a TRISO-fueled pebble is inserted into the nuclear core and subsequently removed. The levels of burnup are measured based on the length of time the pebble spends within the core. In our design, each pebble is numbered by multiple digits printed in six locations using Ultra-High Temperature Ceramic paint. Naturally, computer vision techniques can be used to identify and time each pebble based on its digits as it enters and exits the core. We present a deep learning approach that successfully tags each pebble by identifying its digits from a video stream of the entrance and exit of the core. In a multi-step method, we extract only the clearest and most useful views of the pebble's digits to classify as it rolls by. This algorithm is robust against issues that occur for objects in movement such as motion blur, rotations, and glare. We outperform other state-of-the-art optical character recognition (OCR) models that fail to identify digits that are in motion. Our approach creates a safer and more efficient way to measure burnup within a core while contributing to the improvement of nuclear power produced by reactors.