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.

翻译：本文介绍了RaceLens，一种利用先进深度学习和计算机视觉模型对赛车照片进行综合分析的新型应用。所开发的模型在多项任务中展现出高效性，包括检测赛车、识别车号、检测并量化车辆细节以及识别车辆朝向。我们讨论了训练模型所需鲁棒数据集的收集过程，并描述了一种持续增强和改进该数据集的设计方法。本方法利用反馈循环实现模型的持续改进，从而随时间推移提升RaceLens的性能与准确性。研究的重点部分致力于阐述RaceLens的实际应用，着重介绍了其被NASCAR车队在四个赛季中成功部署的经验。我们对系统性能及其对车队战略决策和性能指标的直接影响进行了全面评估。结果凸显了机器智能在竞争激烈且动态变化的赛车领域中的变革潜力，为未来应用树立了先例。