  Deep learning-based object detection algorithms enable the simultaneous
classification and localization of any number of objects in image data. Many of
these algorithms are capable of operating in real-time on high resolution
images, attributing to their widespread usage across many fields. We present an
end-to-end object detection pipeline designed for real-time rare event searches
for the Migdal effect, using high-resolution image data from a state-of-the-art
scientific CMOS camera in the MIGDAL experiment. The Migdal effect in nuclear
scattering, crucial for sub-GeV dark matter searches, has yet to be
experimentally confirmed, making its detection a primary goal of the MIGDAL
experiment. Our pipeline employs the YOLOv8 object detection algorithm and is
trained on real data to enhance the detection efficiency of nuclear and
electronic recoils, particularly those exhibiting overlapping tracks that are
indicative of the Migdal effect. When deployed online on the MIGDAL readout PC,
we demonstrate our pipeline to process and perform the rare event search on 2D
image data faster than the peak 120 frame per second acquisition rate of the
CMOS camera. Applying these same steps offline, we demonstrate that we can
reduce a sample of 20 million camera frames to around 1000 frames while
maintaining nearly all signal that YOLOv8 is able to detect, thereby
transforming a rare search into a much more manageable search. Our studies
highlight the potential of pipelines similar to ours significantly improving
the detection capabilities of experiments requiring rapid and precise object
identification in high-throughput data environments.
