Real-Time Motorbike Detection: AI on the Edge Perspective

Awais Akhtar, Rehan Ahmed, Muhammad Haroon Yousaf and Sergio A. Velastin ()
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Awais Akhtar: Department of Computer Engineering, University of Engineering and Technology, Taxila 47080, Pakistan
Rehan Ahmed: School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
Muhammad Haroon Yousaf: Department of Computer Engineering, University of Engineering and Technology, Taxila 47080, Pakistan
Sergio A. Velastin: School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK

Mathematics, 2024, vol. 12, issue 7, 1-17

Abstract: Motorbikes are an integral part of transportation in emerging countries, but unfortunately, motorbike users are also one the most vulnerable road users (VRUs) and are engaged in a large number of yearly accidents. So, motorbike detection is very important for proper traffic surveillance, road safety, and security. Most of the work related to bike detection has been carried out to improve accuracy. If this task is not performed in real-time then it loses practical significance, but little to none has been reported for its real-time implementation. In this work, we have looked at multiple real-time deployable cost-efficient solutions for motorbike detection using various state-of-the-art embedded edge devices. This paper discusses an investigation of a proposed methodology on five different embedded devices that include Jetson Nano, Jetson TX2, Jetson Xavier, Intel Compute Stick, and Coral Dev Board. Running the highly compute-intensive object detection model on edge devices (in real-time) is made possible by optimization. As a result, we have achieved inference rates on different devices that are twice as high as GPUs, with only a marginal drop in accuracy. Secondly, the baseline accuracy of motorbike detection has been improved by developing a custom network based on YoloV5 by introducing sparsity and depth reduction. Dataset augmentation has been applied at both image and object levels to enhance robustness of detection. We have achieved 99% accuracy as compared to the previously reported 97% accuracy, with better FPS. Additionally, we have provided a performance comparison of motorbike detection on the different embedded edge devices, for practical implementation.

Keywords: object detection; motorbike detection; edge devices; real-time; traffic surveillance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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