  This paper presents RaceLens, a novel application utilizing advanced deep
learning and computer vision models for comprehensive analysis of racing
photos. The developed models have demonstrated their efficiency in a wide array
of tasks, including detecting racing cars, recognizing car numbers, detecting
and quantifying car details, and recognizing car orientations. We discuss the
process of collecting a robust dataset necessary for training our models, and
describe an approach we have designed to augment and improve this dataset
continually. Our method leverages a feedback loop for continuous model
improvement, thus enhancing the performance and accuracy of RaceLens over time.
A significant part of our study is dedicated to illustrating the practical
application of RaceLens, focusing on its successful deployment by NASCAR teams
over four seasons. We provide a comprehensive evaluation of our system's
performance and its direct impact on the team's strategic decisions and
performance metrics. The results underscore the transformative potential of
machine intelligence in the competitive and dynamic world of car racing,
setting a precedent for future applications.
