  Radio loud active galactic nuclei (RLAGNs) are often morphologically complex
objects that can consist of multiple, spatially separated, components.
Astronomers often rely on visual inspection to resolve radio component
association. However, applying visual inspection to all the hundreds of
thousands of well-resolved RLAGNs that appear in the images from the Low
Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) at $144$ MHz, is a
daunting, time-consuming process, even with extensive manpower.
  Using a machine learning approach, we aim to automate the radio component
association of large ($> 15$ arcsec) radio components.
  We turned the association problem into a classification problem and trained
an adapted Fast region-based convolutional neural network to mimic the expert
annotations from the first LoTSS data release. We implemented a rotation data
augmentation to reduce overfitting and simplify the component association by
removing unresolved radio sources that are likely unrelated to the large and
bright radio components that we consider using predictions from an existing
gradient boosting classifier.
  For large ($> 15$ arcsec) and bright ($> 10$ mJy) radio components in the
LoTSS first data release, our model provides the same associations for
$85.3\%\pm0.6$ of the cases as those derived when astronomers perform the
association manually. When the association is done through public crowd-sourced
efforts, a result similar to that of our model is attained.
  Our method is able to efficiently carry out manual radio-component
association for huge radio surveys and can serve as a basis for either
automated radio morphology classification or automated optical host
identification. This opens up an avenue to study the completeness and
reliability of samples of radio sources with extended, complex morphologies.
