AI Development with Richard Petty Motorsports

By Snigdha Bhardwaj, Anuj Gupta, Jon Hahn and Chris Infanti

Recent advances in AI deep learning have made image recognition possible at scale. Deep learning classifies objects in images by leveraging multiple layers of artificial neural networks where each layer is responsible for extracting one or more feature of the image. In this white paper, you will learn how WWT trained and implemented a neural network to identify, classify and sort images of NASCAR race cars to give NASCAR driver Bubba Wallace and Richard Petty Motorsports insight into the driving behavior of competitors during a race.

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Abstract

In this paper we explore the use of AI to perform an image sorting task for a use case in NASCAR. Currently, the Richard Petty Motorsports (RPM) team acquires over 10,000 images per race and needs to sort them real-time to find the ones that contain the RPM car. This task is quite time consuming for an RPM team member, wasting valuable resources that could be deployed on more critical tasks. With AI, we can do this task quickly and with a low error rate. In addition to isolating just the RPM car, a large pool of cars can be detected and sorted accordingly. Starting with an unlabeled data set of thousands of images across several races, we trained and implemented a neural network to identify, classify and sort images with a high degree of accuracy.

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