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.

本研究介绍了RaceLens，一种利用先进的深度学习和计算机视觉模型进行赛车图片全面分析的新应用。我们讨论了收集强大数据集以训练模型的过程，并描述了我们设计的一种持续改进数据集的方法。该方法通过反馈循环不断改善模型性能和准确性。我们详细评估了RaceLens系统的性能和其对NASCAR车队战略决策和绩效指标的直接影响，结果突显了机器智能在竞争激烈和动态的赛车世界中具有的变革潜力，并为未来的应用树立了先例。

赛事镜头：基于机器智能的赛车照片分析应用