Automatic Recognition of Helmetless Bike Rider License Plate Using Deep Learning

Keerthi, K. V. L.; Teja, V. Krishna; Sekhar, P. Chandra; Shankar, T.

doi:10.1007/978-981-33-6862-0_37

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…So, the potential challenge that we have identified is in determining how we can make a neural network-based motorbike detection system more cost-efficient for real-time and practical implementation, using off-the-shelf embedded hardware. Regarding bike number plate recognition, it is worth noting that OCR (Optical Character Recognition) is a thoroughly researched technique, as evidenced by numerous studies, e.g., [1][2][3]. While we recognize the potential advantages of integrating number plate detection with OCR capabilities into our research, our primary objective in this study was to enhance motorbike detection using deep learning models specifically designed for edge devices that can be a precursor to OCR-based plate recognition and association to a specific motorbike.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Motorbike Detection: AI on the Edge Perspective

Akhtar,

Ahmed,

Yousaf

et al. 2024

Mathematics

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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.

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Section: Introductionmentioning

confidence: 99%