Abstract
In today’s complex and interconnected transportation ecosystem, real-time vehicle sensing is critical for a complex and interconnected transportation ecosystem built on advanced technology networks of intelligent systems spanning a wide range of applications such as autonomous vehicles, traffic monitoring, and advanced driver assistance systems. This study utilizes machine learning approaches to create a pipeline for vehicle identification and classification. Count the number of cars in a frame and divide them into two categories: SUVs and sedans. This article requires knowledge of machine learning fundamentals, deep learning, convolutional networks, and transfer learning. In this paper the pipeline is broken down in order to build and implement a computer vision pipeline.
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Acknowledgements
It would be our utmost pleasure to express our sincere thanks to our guide Prof. Rakhi Bharadwaj who gave us the opportunity to do more research on the topic “Traffic Surveillance and Vehicle Detection ML pipeline using YOLO and MobileNet transfer learning”, which also helped us in doing plenty of ideas and that we came to understand about such a lot of new things.
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Bharadwaj, R., Thombre, A., Patekar, U., Gaikwad, Y., Suri, S. (2023). Traffic Surveillance and Vehicle Detection YOLO and MobileNet-Based ML Pipeline Transfer Learning. In: Suma, V., Lorenz, P., Baig, Z. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 672. Springer, Singapore. https://doi.org/10.1007/978-981-99-1624-5_56
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DOI: https://doi.org/10.1007/978-981-99-1624-5_56
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