Title:
Real-time detection of traffic signs on mobile devices using deep learning
Real-time detection of traffic signs on mobile devices using deep learning
Author(s)
El Bouzekraoui, Younes
Advisor(s)
Zhang, Xiuwei
James, Yi-Chang Tsai
Kira, Zsolt
James, Yi-Chang Tsai
Kira, Zsolt
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Abstract
The rapid advancement in object detection (OD) models has enabled a multitude of applications
in computer vision, yet the deployment on resource-constrained devices, such as
smartphones and single-board computers like the Raspberry Pi or NVIDIA Jetson, remains
challenging due to hardware limitations. This work proposes a methodology to address
the issue by implementing quantization techniques on a YOLO (You Only Look Once)
model specifically adapted for the detection of stop signs. The intention is to improve
road safety by allowing low-end devices to recognize effectively and preemptively stop
signs, thereby potentially reducing the likelihood of traffic-related incidents. We present
a compressed YOLO model tailored for efficient operation on these devices, trained on a
dataset comprised exclusively of stop sign images to optimize detection accuracy within
this constrained context. We apply quantization to reduce the precision of the model’s parameters.
The performance of the resulting model is rigorously evaluated in terms of its
frames-per-second (FPS) and Mean Average Precision (mAP), metrics that balance operational
efficiency with detection reliability. Our findings demonstrate that the model, once
quantized, maintains a high mAP while achieving a significant improvement in FPS when
compared to its uncompressed counterpart. These results not only reinforce the viability
of deploying advanced object detection models on low-resource devices but also provide
a framework for similar adaptations of YOLO models for various real-world applications
where resource efficiency is required.
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Date Issued
2023-12-13
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