A Deep Learning Based Traffic Sign Detection for Intelligent Transportation Systems

2021 ◽  
pp. 129-137
Author(s):  
Bao-Long Le ◽  
Gia-Huy Lam ◽  
Xuan-Vinh Nguyen ◽  
The-Manh Nguyen ◽  
Quoc-Loc Duong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection

scholarly journals Traffic Sign Detection for Intelligent Transportation Systems: A Survey

2021 ◽  
Vol 229 ◽  
pp. 01006
Author(s):  
Ayoub Ellahyani ◽  
Ilyas El Jaafari ◽  
Said Charfi
Keyword(s):  
Intelligent Transportation Systems ◽  
Color Space ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Detection Methods ◽  
Data Sets ◽  
Traffic Sign ◽  
Current State ◽  
Sign Detection ◽  
Traffic Sign Detection

Recently, intelligent transportation systems (ITS) attracts more and more attention for its wide applications. Traffic sign detection and recognition (TSDR) system is an essential task of ITS. It enhances the safety by informing the drivers about the current state of traffic signs and offering valuable information about precautions. This paper reviews the popular traffic sign detection methods (TSD) prevalent in recent literature. The methods are divided into color-based, shape-based, and machine learning based ones. Color space, segmentation method, features, and shape detection method are the terms considered in the review of the detection module. The paper presents a comparison between these methods. Furthermore, a list of publicly available data sets and a discussion on possible future works are provided.

scholarly journals Fast Traffic Sign Detection Approach Based on Lightweight Network and Multilayer Proposal Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Hoanh Nguyen
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
State Of The Art ◽  
Transportation Systems ◽  
Traffic Sign ◽  
Feature Map ◽  
Current State ◽  
Detection Approach ◽  
Sign Detection ◽  
Traffic Sign Detection

Vision-based traffic sign detection plays a crucial role in intelligent transportation systems. Recently, many approaches based on deep learning for traffic sign detection have been proposed and showed better performance compared with traditional approaches. However, due to difficult conditions in driving environment and the size of traffic signs in traffic scene images, the performance of deep learning-based methods on small traffic sign detection is still limited. In addition, the inference speed of current state-of-the-art approaches on traffic sign detection is still slow. This paper proposes a deep learning-based approach to improve the performance of small traffic sign detection in driving environments. First, a lightweight and efficient architecture is adopted as the base network to address the issue of the inference speed. To enhance the performance on small traffic sign detection, a deconvolution module is adopted to generate an enhanced feature map by aggregating a lower-level feature map with a higher-level feature map. Then, two improved region proposal networks are used to generate proposals from the highest-level feature map and the enhanced feature map. The proposed improved region proposal network is designed for fast and accuracy proposal generation. In the experiments, the German Traffic Sign Detection Benchmark dataset is used to evaluate the effectiveness of each enhanced module, and the Tsinghua-Tencent 100K dataset is used to compare the effectiveness of the proposed approach with other state-of-the-art approaches on traffic sign detection. Experimental results on Tsinghua-Tencent 100K dataset show that the proposed approach achieves competitive performance compared with current state-of-the-art approaches on traffic sign detection while being faster and simpler.

scholarly journals TRAFFIC SIGN INVENTORY FROM GOOGLE STREET VIEW IMAGES

2016 ◽  
Vol XLI-B4 ◽  
pp. 243-246
Author(s):  
Victor J. D. Tsai ◽  
Jyun-Han Chen ◽  
Hsun-Sheng Huang
Keyword(s):  
Intelligent Transportation Systems ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Traffic Sign ◽  
Google Street View ◽  
Street View ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign detection and recognition (TSDR) has drawn considerable attention on developing intelligent transportation systems (ITS) and autonomous vehicle driving systems (AVDS) since 1980’s. Unlikely to the general TSDR systems that deal with real-time images captured by the in-vehicle cameras, this research aims on developing techniques for detecting, extracting, and positioning of traffic signs from Google Street View (GSV) images along user-selected routes for low-cost, volumetric and quick establishment of the traffic sign infrastructural database that may be associated with Google Maps. The framework and techniques employed in the proposed system are described.

scholarly journals TRAFFIC SIGN INVENTORY FROM GOOGLE STREET VIEW IMAGES

2016 ◽  
Vol XLI-B4 ◽  
pp. 243-246 ◽  
Author(s):  
Victor J. D. Tsai ◽  
Jyun-Han Chen ◽  
Hsun-Sheng Huang
Keyword(s):  
Intelligent Transportation Systems ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Traffic Sign ◽  
Google Street View ◽  
Street View ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign detection and recognition (TSDR) has drawn considerable attention on developing intelligent transportation systems (ITS) and autonomous vehicle driving systems (AVDS) since 1980’s. Unlikely to the general TSDR systems that deal with real-time images captured by the in-vehicle cameras, this research aims on developing techniques for detecting, extracting, and positioning of traffic signs from Google Street View (GSV) images along user-selected routes for low-cost, volumetric and quick establishment of the traffic sign infrastructural database that may be associated with Google Maps. The framework and techniques employed in the proposed system are described.

scholarly journals Light Field Image Quality Enhancement by a Lightweight Deformable Deep Learning Framework for Intelligent Transportation Systems

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1136
Author(s):  
David Augusto Ribeiro ◽  
Juan Casavílca Silva ◽  
Renata Lopes Rosa ◽  
Muhammad Saadi ◽  
Shahid Mumtaz ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Intelligent Transportation Systems ◽  
Light Field ◽  
State Of The Art ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Machine Learning Algorithms ◽  
Learning Framework ◽  
Reconstruction Methods

Light field (LF) imaging has multi-view properties that help to create many applications that include auto-refocusing, depth estimation and 3D reconstruction of images, which are required particularly for intelligent transportation systems (ITSs). However, cameras can present a limited angular resolution, becoming a bottleneck in vision applications. Thus, there is a challenge to incorporate angular data due to disparities in the LF images. In recent years, different machine learning algorithms have been applied to both image processing and ITS research areas for different purposes. In this work, a Lightweight Deformable Deep Learning Framework is implemented, in which the problem of disparity into LF images is treated. To this end, an angular alignment module and a soft activation function into the Convolutional Neural Network (CNN) are implemented. For performance assessment, the proposed solution is compared with recent state-of-the-art methods using different LF datasets, each one with specific characteristics. Experimental results demonstrated that the proposed solution achieved a better performance than the other methods. The image quality results obtained outperform state-of-the-art LF image reconstruction methods. Furthermore, our model presents a lower computational complexity, decreasing the execution time.

Traffic Sign Detection and Recognition using Deep Learning

2021 ◽  
Author(s):  
Rudri Mahesh Oza ◽  
Angelina Geisen ◽  
Taehyung Wang
Keyword(s):  
Deep Learning ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition
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