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US road sign detection and Visibility Estimation using Artificial Intelligence Techniques

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第1期15卷 604-613页

作者： Abukhait, Jafar Tafila Tech Univ Dept Comp & Commun Engn Tafila Jordan

This paper presents a fully-automated system for detecting road signs in the United States and assess their visibility during daytime from the perspective of the driver using images captured by an in-vehicle camera. The system deploys YOLOv8 to build a multi-label detection model and then, calculates various readability and detectability factors, including the simplicity of the surroundings, potential obstructions, and the angle at which the road sign is positioned, to determine the overall visibility of the sign. This proposed system can be integrated into Driver Assistance Systems (DAS) to manage the information delivered to drivers, as an excess of information could potentially distract them. road signs are categorized based on their visibility levels, allowing Driver Assistance Systems to caution drivers about signs that may have lower visibility but are of significant importance. The system comprises four main stages: 1) identifying road signs using YOLOv8;2) segmenting the surrounding areas;3) measuring visibility parameters;and 4) determining visibility levels through fuzzy logic inference system. This paper introduces a visibility estimation system for road signs specifically tailored to the United States. Experimental results showcase the system's effectiveness. The visibility levels generated by the proposed system were subjectively compared to decisions made by human experts, revealing a substantial agreement between the two approaches.

关键词： road sign detection YOLOv8 driver assistance system fuzzy logic detectability visibility estimation

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Development of Traffic Light and road sign detection and Recognition Using Deep Learning

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第10期15卷 942-952页

作者： De Guia, Joseph M. Deveraj, Madhavi Mapua Univ Sch Informat Technol SOIT Manila Philippines Nanyang Technol Univ Energy Res Inst ERIN Singapore Singapore

Traffic light and road sign violations significantly contribute to traffic accidents, particularly at intersections in high-density urban areas. To address these challenges, this research focuses on enhancing the accuracy, robustness, and reliability of Autonomous Vehicle (AV) perception systems using advanced deep learning techniques. The novelty of this study lies in the comprehensive development and evaluation of real-time traffic light and road sign detection systems, comparing state-ofthe-art models including YOLOv3, YOLOv5, and YOLOv7. The models were rigorously tested in a controlled offline environment using the Nvidia Titan RTX, followed by extensive field testing on an AV test vehicle equipped with sensor suite and Nvidia RTX GPU. The testing was conducted across complex urban driving scenarios at the CETRAN proving test track, JTC Cleantech Park, and NTU Singapore campus. The traffic light detection and recognition (TLR) results demonstrate that YOLOv7 outperforms YOLOv5 and YOLOv3, achieving a mean Average Precision (mAP@0.5) of 93%, even under challenging conditions like poor lighting and occlusions. While the traffic road sign detection (TSD) mAP@0.5 of 96%. This superior performance highlights the potential of YOLOv7 in enhancing AV safety and reliability. The conclusions underscore the effectiveness of YOLOv7 for real-time detection in AV perception systems, offering crucial insights for future research. Potential implications include the development of more robust and accurate AV systems, capable of safely navigating complex urban environments.

关键词： Artificial intelligence autonomous vehicle traffic light recognition road sign detection YOLO real-time object detection

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A Lightweight Model for road sign detection Based on Improved-YOLOv8 9

A Lightweight Model for Road Sign Detection Based on Improve...

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9th International Conference on Electronic Technology and Information Science (ICETIS)

作者： Wang, Bingxuan Wang, Gengzhao Lanzhou Univ Sch Informat Sci & Engn Lanzhou Peoples R China

ISBN: (纸本)9798350388350;9798350388343

In recent years, with the continuous development of autonomous driving technology, target detection algorithms have also begun to be used in road environment detection of autonomous driving technology. However, this application scenario puts forward higher requirements on the real-time performance of the algorithm due to its particularity. To solve this problem, we created a lightweight model based on the improved YOLOv8 to detect common traffic signs on the road. Compared with the popular YOLOv8n, this model uses the PP-LCNet module to replace the original backbone network of the YOLO algorithm, which not only greatly reduces the amount of parameters and floating point accuracy required for the entire model, but also takes into account both inference speed and model performance. In order to prove the reliability and effectiveness of the model, this article compares it with YOLOv8n through experiments. The experimental results show that the floating point operation times and training time of the model are reduced by 36.6% and 43.3% respectively, while the performance of the original model is almost guaranteed. This model greatly improves the real-time performance of the model while ensuring model performance, and is expected to be applied in autonomous driving scenarios.

关键词： YOLOv8 Autonomous driving PP-LCNet road sign detection Lightweight model

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Empirical validation of federated learning with YOLO v7-tiny for road sign detection: A simulation-based comparative study

Empirical validation of federated learning with YOLO v7-tiny...

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作者： Yizhen Bi The Department of Electrical and Electronic Engineering The University of Manchester

Detecting road signs is a critical component in the development of intelligent driving systems. While centralized machine learning approaches have demonstrated potential in this field, the untapped potential of Federated Learning warrants exploration. This research aims to bridge this gap by examining the feasibility of applying Federated Learning within edge Artificial Intelligence(AI) computing environments for the purpose of road sign *** the You Only Look Once(YOLO) v7-tiny model and a range of experimental parameters demonstrates that Federated Learning is viable and outperforms centralized approaches under specific conditions. The study's empirical analysis highlights the sensitivity of detection accuracy to varying experimental parameters. The study contributes to the existing literature by establishing the efficacy of Federated Learning in road sign detection, particularly in edge AI settings constrained by hardware limitations and privacy concerns. However, the study acknowledges limitations, including the lack of deployment on actual edge AI devices and a restricted range of experimental parameters. Future research should aim for more exhaustive experiments with broader datasets, diverse parameters, and real-world edge AI *** findings offer valuable insights for future implementations in intelligent automotive systems.

关键词： Federated Learning road sign detection YOLO v7-tiny

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Deep Neural Networks for road sign detection and Embedded Modeling Using Oblique Aerial Images

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REMOTE SENSING 2021年第5期13卷 879-879页

作者： Mao, Zhu Zhang, Fan Huang, Xianfeng Jia, Xiangyang Gong, Yiping Zou, Qin Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R 129 Luoyu Rd Wuhan 430079 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

Oblique photogrammetry-based three-dimensional (3D) urban models are widely used for smart cities. In 3D urban models, road signs are small but provide valuable information for navigation. However, due to the problems of sliced shape features, blurred texture and high incline angles, road signs cannot be fully reconstructed in oblique photogrammetry, even with state-of-the-art algorithms. The poor reconstruction of road signs commonly leads to less informative guidance and unsatisfactory visual appearance. In this paper, we present a pipeline for embedding road sign models based on deep convolutional neural networks (CNNs). First, we present an end-to-end balanced-learning framework for small object detection that takes advantage of the region-based CNN and a data synthesis strategy. Second, under the geometric constraints placed by the bounding boxes, we use the scale-invariant feature transform (SIFT) to extract the corresponding points on the road signs. Third, we obtain the coarse location of a single road sign by triangulating the corresponding points and refine the location via outlier removal. Least-squares fitting is then applied to the refined point cloud to fit a plane for orientation prediction. Finally, we replace the road signs with computer-aided design models in the 3D urban scene with the predicted location and orientation. The experimental results show that the proposed method achieves a high mAP in road sign detection and produces visually plausible embedded results, which demonstrates its effectiveness for road sign modeling in oblique photogrammetry-based 3D scene reconstruction.

关键词： embedded modeling photogrammetry-based 3D city model oblique aerial image stereo vision road sign detection small object detection

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road sign detection and Recognition of Thai Traffic Based on YOLOv3 13th

Road Sign Detection and Recognition of Thai Traffic Based on...

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13th Multi-Disciplinary International Conference on Multi-Disciplinary Trends in Artificial Intelligence (MIWAI)

作者： Thipsanthia, Paitoon Chamchong, Rapeeporn Songram, Panida Mahasarakham Univ Fac Informat POLAR Lab Maha Sarakham Thailand

ISBN: (纸本)9783030337094;9783030337087

This paper aims to apply a YOLOv3 technique for detecting and recognizing Thai traffic signs in real-time environments. The Thai Traffic sign Dataset (TTSD) was collected by car cameras to store the video images using the resolution of 1920 x 1080 pixels using 60 frames per second, and a 1280 x 720 pixels and 30 frames per second. In addition, the data was collected in the rural area of Maha Sarakham Province and Kalasin Province. The dataset was generated and distributed for general traffic sign detection and recognition. Two architectures (YOLOv3 and YOLOv3 Tiny) are compared with 50 classes of road signs and 200 badges in each class, containing 9,357 images. The experiment shows that the mean average precision (mAP) of YOLOv3 (88.10%) is better than YOLOv3 Tiny (80.84%) while the speed of YOLOv3 marginally is better than YOLOv3.

关键词： road sign detection Deep learning Machine learning

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Research on road-sign detection Algorithms Based on Depth Network

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ENGINEERING LETTERS 2023年第1期31卷 19-19页

作者： Gao, Huaixu Tian, Ying Univ Sci & Technol LiaoNing Sch Comp Sci & Software Engn Anshan Peoples R China

Unmanned system research has attracted increasing academic and corporate attention in recent years. Traffic-sign and road detection systems play an important role in unmanned systems. However, current algorithms perform poorly in reading traffic signs from a long distance and cannot satisfy the real-time requirements of accurate and rapid detection. Therefore, Yolov5s-Swin model is proposed in this paper. First, the Swin Transformer module and convolution module are fused, the ResUnit module in the Yolov5s backbone network is improved, the Crswin module is proposed, and the PAFPN in the network is modified to strengthen the ability of the model to capture local feature information and improve network detection accuracy. The Swin Transformer module uses the moved-window attention function to segment the image into different windows of a certain size but cannot establish a connection between neighboring windows. Consequently, we designed a method for hyperbolic window attention. This approach updates the window partition method such that the window block for attention calculation at each pixel changes, which can improve the receptive field, increase the information extraction ability of the target, and solve the information loss caused by decreased resolution during image training. Experimental results show that the proposed improved Yolov5s-Swin model exhibits significantly better detection accuracy than existing models.

关键词： road sign detection Yolov5s Transformer Swin-Transformer

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A Detailed Study on Bangladeshi road sign detection and Recognition 4

A Detailed Study on Bangladeshi Road Sign Detection and Reco...

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4th International Conference on Electrical Information and Communication Technology (EICT)

作者： Ahsan, Sk Md Masudul Das, Sunanda Kumar, Shanto La Tasriba, Zannati Khulna Univ Engn & Technol Dept Comp Sci & Engn Khulna 9203 Bangladesh

ISBN: (纸本)9781728160405

Automatic road sign detection and recognition is crucial for autonomous Driver Assistance Systems (DAS). Numerous road accidents and casualties are reported every year due to the negligence of drivers. The system is mainly developed for autonomous vehicles and drives to warn them about the existing road sign on the road and also to reduce the frequency of accidents throughout the country. road sign detection and recognition is still challenging concerning Bangladesh because of the shortage of big dataset. The dataset for this work has been collected from different regions throughout the country. In this system, the Single Shot Multibox Detector (SSD) is used for automatic detection and recognition of road sign. The Convolutional Neural Network (CNN) is also applied for the recognition purpose. The system is able to achieve the average detection rate of 76.52% and the recognition accuracy of 86.23% using SSD and also accomplish the recognition accuracy of 80.26% while using CNN. Therefore, the proposed system is robust enough to detect and recognize road sign.

关键词： road sign detection road sign Recognition Single Shot Multibox Detector (SSD) Convolutional Neural Network (CNN)

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Applications of Deep Learning to road sign detection in DVR Images 22

Applications of Deep Learning to Road Sign Detection in DVR ...

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22nd IEEE International Symposium on Measurement and Control in Robotics (ISMCR) - Robotics for the Benefit of Humanity

作者： Kuo, Yong-Lin Lin, Shih-Hsun Natl Taiwan Univ Sci & Technol Grad Inst Automat & Control Taipei Taiwan

ISBN: (纸本)9781728148984

This paper applies deep learning to road sign detection based on the images obtained from DVRs. Three convolutional neural network based state-of-the-art real-time object detection systems are explored for road sign detection, where the road sign plates are vertical or horizontal. First, collect the images of street scenes including road signs as an image library, which is divided into three parts for training, validation and testing. Secondly, according to the exposure values (EVs) of images, divide the images into three categories, high exposures labeled as EV-H, middle exposures labeled as EV-M, and low exposures labeled as EV-L. Thirdly, the road signs in each image are labeled to perform supervised learning. Fourthly, apply three object detection systems to road sign detection, You Only Look Once (YOLO) version 2 (v2), YOLO version 3 (v3), and Single-Step Multi-Box detection (SSD), where an improved method based on YOLO v2 is proposed by dividing the images into three kinds of meshes and recalculating the values of the anchor boxes by k-means algorithm. Finally, analyze the mean average precisions and the frame rates of all images to conclude the best models for the three conditions EV-L, EV-M and EV-H.

关键词： road sign detection deep learning convolutional neural network

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From Synthetic Images Towards Detectors of Real Objects: A Case Study on road sign detection 18th

From Synthetic Images Towards Detectors of Real Objects: A C...

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18th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Klesk, Przemyslaw West Pomeranian Univ Technol Fac Comp Sci & Informat Technol Ul Zolnierska 49 PL-71210 Szczecin Poland

ISBN: (纸本)9783030209155;9783030209148

Large amounts of suitably marked images are required to feed a learning algorithm when building a detector. The process of collecting images and then marking locations of target objects in them is arduous. One could potentially speed up this process if real images for a given application were replaced by images generated synthetically, and coordinates of targets were simply imposed, rather then discovered manually. Despite the appeal of such an automatization, questions arise regarding the usefulness of systems built this way in real operating conditions. In particular, the obvious violation of i.i.d. principle might result in higher error rates at the testing stage. In this paper we provide an experimental study of the above approach, taking up road sign detection as an example. We generate synthetic training scenes by laying road sign icons randomly over a set of backgrounds with additional perturbations (rotations, brightness changes, blurring, sharpening, noise). Ensemble learning is then carried out using a RealBoost algorithm with shallow decision trees. Haar-like features or Fourier moments constitute the direct input information extracted from images. In both cases we support the computations with suitable integral images. Accuracy of resulting detectors is finally tested on real images.

关键词： Synthetic images road sign detection Haar-like features Fourier moments Integral images Boosting

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