  In the present work, accurate determination of single-particle ignition is
focused on using high-speed optical diagnostics combined with machine learning
approaches. Ignition of individual particles in a laminar flow reactor are
visualized by simultaneous 10 kHz OH-LIF and DBI measurements. Two coal
particle sizes of 90-125{\mu}m and 160-200{\mu}m are investigated in
conventional air and oxy-fuel conditions with increasing oxygen concentrations.
Ignition delay times are first evaluated with threshold methods, revealing
obvious deviations compared to the ground truth detected by the human eye.
Then, residual networks (ResNet) and feature pyramidal networks (FPN) are
trained on the ground truth and applied to predict the ignition time.~Both
networks are capable of detecting ignition with significantly higher accuracy
and precision. Besides, influences of input data and depth of networks on the
prediction performance of a trained model are examined.~The current study shows
that the hierarchical feature extraction of the convolutions networks clearly
facilitates data evaluation for high-speed optical measurements and could be
transferred to other solid fuel experiments with similar boundary conditions.
