  This paper explores a semi-supervised object detection approach to detect hot
spots on the internal wall of Tokamaks. A huge amount of data is produced
during an experimental campaign by the infrared (IR) viewing systems used to
monitor the inner thermal shields during machine operation. The amount of data
to be processed and analysed is such that protecting the first wall is an
overwhelming job. Automatizing this job with artificial intelligence (AI) is an
attractive solution, but AI requires large labelled databases which are not
readily available for Tokamak walls. Semi-supervised learning (SSL) is a
possible solution to being able to train deep learning models with a small
amount of labelled data and a large amount of unlabelled data. SSL is explored
as a possible tool to rapidly adapt a model trained on an experimental campaign
A of Tokamak WEST to a new experimental campaign B by using labelled data from
campaign A, a little labelled data from campaign B and a lot of unlabelled data
from campaign B. Model performances are evaluated on two labelled datasets and
two methods including semi-supervised learning. Semi-supervised learning
increased the mAP metric by over six percentage points on the first smaller
scale database and over four percentage points on the second larger scale
dataset depending on the employed method.
