scholarly journals Deep Learning for Traffic Sign Recognition Based on Spatial Pyramid Pooling with Scale Analysis

2020 ◽  
Vol 10 (19) ◽  
pp. 6997 ◽  
Author(s):  
Shao-Kuo Tai ◽  
Christine Dewi ◽  
Rung-Ching Chen ◽  
Yan-Ting Liu ◽  
Xiaoyi Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicle ◽  
Comparative Investigation ◽  
Dual Function ◽  
Test Accuracy ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Driver Support Systems ◽  
Spatial Pyramid Pooling ◽  
Spatial Pyramid

In the area of traffic sign detection (TSD) methods, deep learning has been implemented and achieves outstanding performance. The detection of a traffic sign, as it has a dual function in monitoring and directing the driver, is a big concern for driver support systems. A core feature of autonomous vehicle systems is the identification of the traffic sign. This article focuses on the prohibitive sign. The objective is to detect in real-time and reduce processing time considerably. In this study, we implement the spatial pyramid pooling (SPP) principle to boost Yolo V3’s backbone network for the extraction of functionality. Our work uses SPP for more comprehensive learning of multiscale object features. Then, perform a comparative investigation of Yolo V3 and Yolo V3 SPP across various scales to recognize the prohibitory sign. Comparisons with Yolo V3 SPP models reveal that their mean average precision (mAP) is higher than Yolo V3. Furthermore, the test accuracy findings indicate that the Yolo V3 SPP model performs better than Yolo V3 for different sizes.

scholarly journals Evaluation of Robust Spatial Pyramid Pooling Based on Convolutional Neural Network for Traffic Sign Recognition System

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 889 ◽  
Author(s):  
Christine Dewi ◽  
Rung-Ching Chen ◽  
Shao-Kuo Tai
Keyword(s):  
Autonomous Driving ◽  
Recognition System ◽  
Main Role ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Average Accuracy ◽  
Spatial Pyramid Pooling ◽  
Object Features ◽  
Spatial Pyramid

Traffic sign recognition (TSR) is a noteworthy issue for real-world applications such as systems for autonomous driving as it has the main role in guiding the driver. This paper focuses on Taiwan’s prohibitory sign due to the lack of a database or research system for Taiwan’s traffic sign recognition. This paper investigates the state-of-the-art of various object detection systems (Yolo V3, Resnet 50, Densenet, and Tiny Yolo V3) combined with spatial pyramid pooling (SPP). We adopt the concept of SPP to improve the backbone network of Yolo V3, Resnet 50, Densenet, and Tiny Yolo V3 for building feature extraction. Furthermore, we use a spatial pyramid pooling to study multi-scale object features thoroughly. The observation and evaluation of certain models include vital metrics measurements, such as the mean average precision (mAP), workspace size, detection time, intersection over union (IoU), and the number of billion floating-point operations (BFLOPS). Our findings show that Yolo V3 SPP strikes the best total BFLOPS (65.69), and mAP (98.88%). Besides, the highest average accuracy is Yolo V3 SPP at 99%, followed by Densenet SPP at 87%, Resnet 50 SPP at 70%, and Tiny Yolo V3 SPP at 50%. Hence, SPP can improve the performance of all models in the experiment.

Deep learning–based traffic sign recognition for unmanned autonomous vehicles

2018 ◽  
Vol 232 (5) ◽  
pp. 497-505 ◽  
Author(s):  
Di Zang ◽  
Zhihua Wei ◽  
Maomao Bao ◽  
Jiujun Cheng ◽  
Dongdong Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
The Time Domain

Being one of the key techniques for unmanned autonomous vehicle, traffic sign recognition is applied to assist autopilot. Colors are very important clues to identify traffic signs; however, color-based methods suffer performance degradation in the case of light variation. Convolutional neural network, as one of the deep learning methods, is able to hierarchically learn high-level features from the raw input. It has been proved that convolutional neural network–based approaches outperform the color-based ones. At present, inputs of convolutional neural networks are processed either as gray images or as three independent color channels; the learned color features are still not enough to represent traffic signs. Apart from colors, temporal constraint is also crucial to recognize video-based traffic signs. The characteristics of traffic signs in the time domain require further exploration. Quaternion numbers are able to encode multi-dimensional information, and they have been employed to describe color images. In this article, we are inspired to present a quaternion convolutional neural network–based approach to recognize traffic signs by fusing spatial and temporal features in a single framework. Experimental results illustrate that the proposed method can yield correct recognition results and obtain better performance when compared with the state-of-the-art work.

Autonomous Vehicle Application for Improving Traffic Sign Learning near Ramps

2020 ◽  
Vol 2674 (10) ◽  
pp. 158-166
Author(s):  
Zhenhua Zhang ◽  
Leon Stenneth ◽  
Xiyuan Liu
Keyword(s):  
State Of The Art ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Speed Reduction ◽  
Sign Recognition ◽  
Path Information ◽  
Real World Applications ◽  
Location Adjustment

The state-of-the-art traffic sign recognition (TSR) algorithms are designed to recognize the textual information of a traffic sign at over 95% accuracy. Even though, they are still not ready for complex roadworks near ramps. In real-world applications, when the vehicles are running on the freeway, they may misdetect the traffic signs for the ramp, which will become inaccurate feedback to the autonomous driving applications and result in unexpected speed reduction. The misdetection problems have drawn minimal attention in recent TSR studies. In this paper, it is proposed that the existing TSR studies should transform from the point-based sign recognition to path-based sign learning. In the proposed pipeline, the confidence of the TSR observations from normal vehicles can be increased by clustering and location adjustment. A supervised learning model is employed to classify the clustered learned signs and complement their path information. Test drives are conducted in 12 European countries to calibrate the models and validate the path information of the learned sign. After model implementation, the path accuracy over 1,000 learned signs can be increased from 75.04% to 89.80%. This study proves the necessity of the path-based TSR studies near freeway ramps and the proposed pipeline demonstrates a good utility and broad applicability for sensor-based autonomous vehicle applications.

scholarly journals Exploiting synergies of mobile mapping sensors and deep learning for traffic sign recognition systems

2017 ◽  
Vol 89 ◽  
pp. 286-295 ◽  
Author(s):  
Álvaro Arcos-García ◽  
Mario Soilán ◽  
Juan A. Álvarez-García ◽  
Belén Riveiro
Keyword(s):  
Deep Learning ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Mobile Mapping ◽  
Sign Recognition ◽  
Recognition Systems
Sign in / Sign up

Export Citation Format

Share Document