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Advanced Vehicle Detection Heads-Up Display with TensorFlow Lite

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Proceedings of Third International Conference on Sustainable Expert Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 587))

Abstract

The real-time heads-up display (HUD) will enable the human operator to have a new sense and perspective to driving. The proposed Heads-up display (HUD) detects vehicles, civilians, and obstacles by allowing the user to stay focused on driving. It has a navigator map to keep the user aware of their current location and helps to track their movement. It also tracks speed with an overlayed speedometer and offers fail-safe emergency protocol to alert the emergency contacts when a crash is detected. All these features are accessible by almost everyone who have access to a mobile device and is very cost-effective. The Heads-up display serves as an effective medium between the car and human operator. The operator can set any of the three models: SSD, YOLO v2 and v3 based on their device performance level. If it is a lower-end device, the SSD model performs faster and efficient. If it is a high-end device, the performance can be improved by using YOLO higher version models.

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Correspondence to N. Sabiyath Fatima .

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Haris, K.M., Fatima, N.S., Albeez, S.A. (2023). Advanced Vehicle Detection Heads-Up Display with TensorFlow Lite. In: Shakya, S., Balas, V.E., Haoxiang, W. (eds) Proceedings of Third International Conference on Sustainable Expert Systems . Lecture Notes in Networks and Systems, vol 587. Springer, Singapore. https://doi.org/10.1007/978-981-19-7874-6_47

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