  Nuclear reactor inspections are critical to ensure the safety and reliability
of a nuclear facility's operation. In Canada, Ultrasonic Testing (UT) is used
to inspect the health of pressure tubes which are part of Canada's Deuterium
Uranium (CANDU) reactor's fuel channels. Currently, analysis of UT scans is
performed by manual visualization and measurement to locate, characterize, and
disposition flaws. Therefore, there is motivation to develop an automated
method that is fast and accurate. In this paper, a proof of concept (PoC) that
automates the detection of flaws in nuclear fuel channel UT scans using a
convolutional neural network (CNN) is presented. The CNN model was trained
after constructing a dataset using historical UT scans and the corresponding
inspection results. The requirement for this prototype was to identify the
location of at least a portion of each flaw in UT scans while minimizing false
positives (FPs). The proposed CNN model achieves this target by automatically
identifying at least a portion of each flaw where further manual analysis is
performed to identify the width, the length, and the type of the flaw.
