  Astronomers require efficient automated detection and classification
pipelines when conducting large-scale surveys of the (optical) sky for variable
and transient sources. Such pipelines are fundamentally important, as they
permit rapid follow-up and analysis of those detections most likely to be of
scientific value. We therefore present a deep learning pipeline based on the
convolutional neural network architecture called $\texttt{MeerCRAB}$. It is
designed to filter out the so called 'bogus' detections from true astrophysical
sources in the transient detection pipeline of the MeerLICHT telescope. Optical
candidates are described using a variety of 2D images and numerical features
extracted from those images. The relationship between the input images and the
target classes is unclear, since the ground truth is poorly defined and often
the subject of debate. This makes it difficult to determine which source of
information should be used to train a classification algorithm. We therefore
used two methods for labelling our data (i) thresholding and (ii) latent class
model approaches. We deployed variants of $\texttt{MeerCRAB}$ that employed
different network architectures trained using different combinations of input
images and training set choices, based on classification labels provided by
volunteers. The deepest network worked best with an accuracy of 99.5$\%$ and
Matthews correlation coefficient (MCC) value of 0.989. The best model was
integrated to the MeerLICHT transient vetting pipeline, enabling the accurate
and efficient classification of detected transients that allows researchers to
select the most promising candidates for their research goals.

Instrumentation and Methods for Astrophysics;Astrophysics of Galaxies;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning