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

RaceLens: A Machine Intelligence-Based Application for Racing Photo Analysis