Paper
23 August 2024 Real-time object detection of traffic signs using SSDNet: a case study in Shanghai
Yujia Qiu
Author Affiliations +
Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 132500T (2024) https://doi.org/10.1117/12.3038824
Event: 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia
Abstract
The objective detection of traffic signs in scene images is a research problem that entails identifying traffic sign targets within a given visual context. In recent years, this pursuit has found extensive applications in road scene monitoring systems and autonomous driving technologies. Consequently, the optimization of traffic sign object detection holds paramount significance. This study focuses on establishing a model for real-time detection of traffic signs in the urban environment of Shanghai. Leveraging a dedicated traffic sign dataset, training was conducted utilizing the SSD (Single Shot Multibox Detector) neural network for object detection. The results indicate an achieved error rate of approximately 5%. With few exceptions related to challenging lighting conditions or instances where the size of the traffic signs proved to be excessively small, the model demonstrated favorable outcomes across the majority of images.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Yujia Qiu "Real-time object detection of traffic signs using SSDNet: a case study in Shanghai", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 132500T (23 August 2024); https://doi.org/10.1117/12.3038824
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KEYWORDS
Object detection

Target detection

Education and training

Visualization

Oceanography

Roads

Detection and tracking algorithms

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