Traffic Sign Detection And Recognition Research Articles

Enhancing autonomous driving safety: A robust traffic sign detection and recognition model TSD-YOLO

As autonomous driving technology rapidly advances, Traffic Sign Detection and Recognition (TSDR) has become pivotal in ensuring the safety and regulatory compliance of autonomous vehicles. Despite progress, existing technologies struggle under challenging conditions such as adverse weather and complex roadway environments. To overcome these obstacles, we introduce a novel model, TSD-YOLO, which leverages Mamba and YOLO technologies to enhance the accuracy and robustness of traffic sign detection. Our innovative YOLO-MAM dual-branch module merges convolutional layer-based local feature extraction with the long-distance dependency capabilities of the State Space Models (SSMs). We conducted experimental validations using the Tsinghua-Tencent 100K (TT-100K) dataset and the Mapillary Traffic Sign Detection (MTSD) dataset, demonstrating our model’s efficacy across various datasets. Furthermore, cross-dataset validations affirm the model’s exceptional generalization and robustness across diverse environments. This study not only bolsters traffic sign detection and recognition in autonomous driving systems but also paves the way for future advancements in autonomous driving technology.

CTM-YOLOv8n: A Lightweight Pedestrian Traffic-Sign Detection and Recognition Model with Advanced Optimization

Traffic-sign detection and recognition (TSDR) is crucial to avoiding harm to pedestrians, especially children, from intelligent connected vehicles and has become a research hotspot. However, due to motion blurring, partial occlusion, and smaller sign sizes, pedestrian TSDR faces increasingly significant challenges. To overcome these difficulties, a CTM-YOLOv8n model is proposed based on the YOLOv8n model. With the aim of extracting spatial features more efficiently and making the network faster, the C2f Faster module is constructed to replace the C2f module in the head, which applies filters to only a few input channels while leaving the remaining ones untouched. To enhance small-sign detection, a tiny-object-detection (TOD) layer is designed and added to the first C2f layer in the backbone. Meanwhile, the seventh Conv layer, eighth C2f layer, and connected detection head are deleted to reduce the quantity of model parameters. Eventually, the original CIoU is replaced by the MPDIoU, which is better for training deep models. During experiments, the dataset is augmented, which contains the choice of categories ‘w55’ and ‘w57’ in the TT100K dataset and a collection of two types of traffic signs around the schools in Tianjin. Empirical results demonstrate the efficacy of our model, showing enhancements of 5.2% in precision, 10.8% in recall, 7.0% in F1 score, and 4.8% in mAP@0.50. However, the number of parameters is reduced to 0.89M, which is only 30% of the YOLOv8n model. Furthermore, the proposed CTM-YOLOv8n model shows superior performance when tested against other advanced TSDR models.

Traffic Sign Board Detection and Voice Alert System

Automated tasks have simplified practically everything we perform in today's world. Drivers often miss signage on the side of the road in an effort to focus on the road, which can be dangerous for them and others. This issue may be avoided if there was a quick way to alert the driver without requiring them to divert their attention. TSDR (Traffic Sign Detection and Recognition) is useful in this situation because it detects and recognizes signs, alerting the motorist to any forthcoming signs. This not only assures road safety, but also puts the driver at ease when driving on unfamiliar or difficult roads. Another issue that arises frequently is the inability to comprehend the sign's meaning. Drivers will no longer struggle to grasp what the sign is saying thanks to this Advanced Driver Assistance Systems (ADAS) application. In this paper, we propose a method for detecting and recognizing traffic signs that uses image processing for detection and an ensemble of Convolutional Neural Networks (LeNet) for recognition. Because LeNet CNN have a high recognition rate, they are ideal for a variety of computer vision tasks. For the CNN implementation, Tensor Flow is employed. And here for object detection we implement yolo frame work On the German data sets, we achieved recognition accuracies of more than 99 percent for circular signs.

Equirectangular Image Data Detection, Segmentation and Classification of Varying Sized Traffic Signs: A Comparison of Deep Learning Methods.

There are known limitations in mobile omnidirectional camera systems with an equirectangular projection in the wild, such as momentum-caused object distortion within images, partial occlusion and the effects of environmental settings. The localization, instance segmentation and classification of traffic signs from image data is of significant importance to applications such as Traffic Sign Detection and Recognition (TSDR) and Advanced Driver Assistance Systems (ADAS). Works show the efficacy of using state-of-the-art deep pixel-wise methods for this task yet rely on the input of classical landscape image data, automatic camera focus and collection in ideal weather settings, which does not accurately represent the application of technologies in the wild. We present a new processing pipeline for extracting objects within omnidirectional images in the wild, with included demonstration in a Traffic Sign Detection and Recognition (TDSR) system. We compare Mask RCNN, Cascade RCNN, and Hybrid Task Cascade (HTC) methods, while testing RsNeXt 101, Swin-S and HRNetV2p backbones, with transfer learning for localization and instance segmentation. The results from our multinomial classification experiment show that using our proposed pipeline, given that a traffic sign is detected, there is above a 95% chance that it is classified correctly between 12 classes despite the limitations mentioned. Our results on the projected images should provide a path to use omnidirectional images with image processing to enable the full surrounding awareness from one image source.

Implementation of Bootstrap Technique in Detection of Road Sign Using Artificial Intelligence

Abstract: In today's world, automated tasks have made nearly everything we perform simpler. In an endeavour to concentrate on the road, drivers frequently overlook signs on the side of a road, which might be dangerous for them and nearby motorists. If there was an rapid means to inform the driver before causing them to change their focus, this problem may be avoided. If TSDR (Traffic Sign Detection and Recognition) identifies and understands signs in this circumstance, it may warn the driver of any impending signs. This not only ensures traffic safety, but also gives the driver peace of mind when traveling through uncharted or challenging roads. Not being able to interpret the sign's meaning is another significant problem. (Traffic Sign Recognition), which is a crucial element of modern driver assistance systems that contributes to driver safety, autonomous vehicle safety, and increased driving comfort. In comparison to previous decades, road conditions have vastly improved in today's globe. The vehicle's speed obviously increased. As a result, there may be opportunities for drivers to disregard necessary traffic signs while driving. This study investigates a systemIt helps drivers see traffic signs and prevent tragedies. TSR is a difficult process, and its accuracy is dependent on two factors: the feature extractor and the classifier.. To do both feature extraction and classification, most current popular algorithms use CNN (Convolutional Neural Network). In this work, we use CNN to the recognition of traffic signs. 43 unique traffic datasets will be used to build the CNN. Sign classes, as well as the TensorFlow library. The findings will indicate a 95% accuracy rate

Long-Tailed Traffic Sign Detection Using Attentive Fusion and Hierarchical Group Softmax

Traffic sign detection and recognition (TSDR) has attracted extensive studies recently due to its broad application prospect in Intelligent Transport Systems. TSDR is still challenging due to the small size of traffic signs in the image. Besides, the traffic signs in the real world exhibit a long-tailed distribution (i.e., data for most categories are scarce while for others are abundant.), which will lead to a significant performance drop of the detection framework. In this paper, we propose a novel traffic sign detection framework to address these challenging problems. In order to detect small traffic signs, we propose an effective adaptive and attentive spatial feature fusion module which learns the spatial attention map to fuse different feature maps at each scale while emphasizing or suppressing the features at different regions. This module can significantly alleviate the inconsistency among features and enhance feature representations of small objects. Furthermore, to address the long-tailed data problem, a hierarchical group softmax head which constructs a label tree to divide categories into different groups is proposed, in this way, categories in each group have relatively similar frequencies, then the softmax is applied in each relatively balanced group to calculate the probability of each category. Extensive experiments conducted on the TT100K and GTSDB datasets demonstrate that the proposed method achieves notable improvement in both the small traffic signs and long-tailed detection problems in TSDR.

PSG-Yolov5: A Paradigm for Traffic Sign Detection and Recognition Algorithm Based on Deep Learning

With the gradual popularization of autonomous driving technology, how to obtain traffic sign information efficiently and accurately is very important for subsequent decision-making and planning tasks. Traffic sign detection and recognition (TSDR) algorithms include color-based, shape-based, and machine learning based. However, the algorithms mentioned above are insufficient for traffic sign detection tasks in complex environments. In this paper, we propose a traffic sign detection and recognition paradigm based on deep learning algorithms. First, to solve the problem of insufficient spatial information in high-level features of small traffic signs, the parallel deformable convolution module (PDCM) is proposed in this paper. PDCM adaptively acquires the corresponding receptive field preserving the integrity of the abstract information through symmetrical branches thereby improving the feature extraction capability. Simultaneously, we propose sub-pixel convolution attention module (SCAM) based on the attention mechanism to alleviate the influence of scale distribution. Distinguishing itself from other feature fusion, our proposed method can better focus on the information of scale distribution through the attention module. Eventually, we introduce GSConv to further reduce the computational complexity of our proposed algorithm, better satisfying industrial application. Experimental results demonstrate that our proposed methods can effectively improve performance, both in detection accuracy and mAP@0.5. Specifically, when the proposed PDCM, SCAM, and GSConv are applied to the Yolov5, it achieves 89.2% mAP@0.5 in TT100K, which exceeds the benchmark network by 4.9%.

A SECI Method Based on Improved YOLOv4 for Traffic Sign Detection and Recognition

Traffic sign detection and recognition (TSDR) has become extraordinarily significant in autonomous driving. To trade-off between high accuracy and fast detection speed, a four-stage method, Selecting-Extracting-Cleaning and Identifying (SECI) based on improved algorithm of You Only Look Once version 4 (YOLOv4) is proposed, including selecting the anchor boxes of traffic signs by using k-means++ clustering algorithm, extracting traffic sign features by using subsampled layer, cleaning the redundant information by amending structure of the Spatial Pyramid Pooling (SPP) and identifying traffic signs by adding auxiliary branches and multi-scale feature fusion. Compared to the origin YOLOv4, the results show that, the SECI method improves the mean average precision (mAP) by 6.71%, the detection speed is improved by 37%, and the F1 score is improved by 13%.

Literature Review on Vision-based Traffic Sign Detection and Recognition using Artificial Intelligence

Abstract: With the expansion in the number of vehicles on the road, the development of technology, and other factors, safety has elevated to the top of automotive industries. Following traffic laws and regulations are essential in Advanced Driver Assistance Systems (ADAS) to prevent collisions. Over the past few decades, traffic sign recognition has gained significant interest due to its quick processing speed and the reduction of human interactions, which lowers risk with the assistance of Artificial Intelligence. While trying to focus on the road, drivers sometimes miss signage on the side of the road, which could be vulnerable for both them and the pedestrians. If the driver could be alerted effectively without having to change their focus, this issue may be avoided. When a sign is detected and recognised, the Traffic Sign Detection and Recognition (TSDR) system alerts the driver to any approaching signs. In addition to ensuring road safety, this also gives drivers a little more comfort while negotiating challenging or unfamiliar roads. This paper provides a comprehensive survey on traffic sign detection and recognition with details of algorithms, architectures, and methods implemented. Based on the investigation of several experts, this literature study was completed and may be used as a future reference

Traffic Sign Detection and Recognition

Street traffic signs give directions, cautioning data, to control driver conduct. Also, these signs give a dependable assurance to protected and helpful driving. The Traffic Sign Detection and Recognition (TSDR) framework is one of the essential applications for Advanced Driver Assistance Systems (ADAS). TSDR has gotten a lot of consideration over the new year’s. Be that as it may, it is as yet a difficult field of picture handling. In this work, the area of visual article discovery, following and acknowledgment in the rush hour gridlock climate is investigated. The essential spotlight is placed on the issue of video-based traffic sign acknowledgment (TSR), which is one of the significant assignments in the contemporary visual driver help frameworks. Certain calculations thusly introduced are utilized for tackling related issues, for example, walker identification or grouping of vehicle models. At the recognition stage a few procedures are broke down and assessed utilizing restrained picture and video datasets. This framework additionally investigates the chance of custom preparation of YOLOv3 based calculation.

Road Sign Detection System using Neural Networks and Tensor Flow

Abstract: Automated tasks have simplified almost everything we perform in today's environment. Due to a desire to focus only on driving, drivers regularly ignore signs placed on the side of the road, which can be harmful to themselves and others. To address this issue, the motorist should be informed in a method that does not require them to divert their concentration. Traffic Sign Detection and Recognition (TSDR) is critical in this case since it alerts the motorist of approaching signals. Not only are roads safer because of this, but motorists also feel more at ease when driving unfamiliar or difficult routes. Another typical issue is inability to read the sign. Driver assistance systems (ADAS) will make it easier for motorists to read traffic signs with the help of this software. We provide a traffic sign detection and recognition system that employs image processing for sign detection and an ensemble of Convolutional Neural Networks (CNNs) for sign recognition. Because of its high recognition rate, CNNs may be used in a wide range of computer vision applications. TensorFlow is used in CNNTSR (Traffic Sign Recognition), a key component of current driving assistance systems that improves driver safety and comfort. TensorFlow is used to implement CNNTSR (Traffic Sign Recognition). This article examines a technology that assists drivers in recognizing traffic signs and avoiding road accidents. Two things determine the accuracy of TSR: the feature extractor and the classifier. Although there are a variety of approaches, most recent algorithms use CNN (Convolutional Neural Network) to do both feature extraction and classification tasks. Using TensorFlow and CNN, we create traffic sign recognition. The CNN will be trained using a dataset of 43 distinct types of traffic signs. The accuracy of the findings will be 95 percent. Keywords: Driver, Tensor flow, Data Sheet, Alert, CNNTSR, ADAS.

Towards Enhancing Traffic Sign Recognition through Sliding Windows.

Automatic Traffic Sign Detection and Recognition (TSDR) provides drivers with critical information on traffic signs, and it constitutes an enabling condition for autonomous driving. Misclassifying even a single sign may constitute a severe hazard, which negatively impacts the environment, infrastructures, and human lives. Therefore, a reliable TSDR mechanism is essential to attain a safe circulation of road vehicles. Traffic Sign Recognition (TSR) techniques that use Machine Learning (ML) algorithms have been proposed, but no agreement on a preferred ML algorithm nor perfect classification capabilities were always achieved by any existing solutions. Consequently, our study employs ML-based classifiers to build a TSR system that analyzes a sliding window of frames sampled by sensors on a vehicle. Such TSR processes the most recent frame and past frames sampled by sensors through (i) Long Short-Term Memory (LSTM) networks and (ii) Stacking Meta-Learners, which allow for efficiently combining base-learning classification episodes into a unified and improved meta-level classification. Experimental results by using publicly available datasets show that Stacking Meta-Learners dramatically reduce misclassifications of signs and achieved perfect classification on all three considered datasets. This shows the potential of our novel approach based on sliding windows to be used as an efficient solution for TSR.

Synthetic Traffic Sign Image Generation Applying Generative Adversarial Networks

Recently, it was shown that convolutional neural networks (CNNs) with suitably annotated training data and results produce the best traffic sign detection (TSD) and recognition (TSR). The whole system’s efficiency is determined by the data collecting process based on neural networks. As a result, the datasets for traffic signs in most nations throughout the globe are difficult to recognize because of their diversity. To address this problem, we must create a synthetic image to enhance our dataset. We apply deep convolutional generative adversarial networks (DCGAN) and Wasserstein generative adversarial networks (Wasserstein GAN, WGAN) to generate realistic and diverse additional training images to compensate for the original image distribution’s data shortage. This study focuses on the consistency of DCGAN and WGAN images created with varied settings. We utilize an actual picture with various numbers and scales for training. Additionally, the Structural Similarity Index (SSIM) and the Mean Square Error (MSE) were used to determine the image’s quality. In our study, we computed the SSIM values between pictures and their corresponding real images. When more training images are used, the images created have a significant degree of similarity to the original image. The results of our experiment reveal that the most leading SSIM values are achieved when 200 total images of [Formula: see text] pixels are utilized as input and the epoch is 2000.

Traffic Sign Detection and Recognition using Image Processing

Abstract: Now a days, automobiles became most convenient mode of transportation for everyone. As we know one of the most important functions, TSDR has become a popular research . It primarily involves the use of vehicle cameras to collect real- time road pictures and then recognize and identify traffic signs seen on the road, therefore delivering correct data to the driving system. With the advancement of science and technology, an increasing number of scholars are turning to deep learning technology to save time in traditional processes. From the training samples, this model can learn the deep features inside the autonomously. The accuracy and great efficiency of detection and identification are the subject of this essay. A deep convolution neural network algorithm is proposed to train traffic sign training sets using Caffe[3], an open-source framework, in order to obtain a model that can classify traffic signs and learn and identify the most critical of these traffic sign features, in order to achieve the goal of identifying traffic signs in the real world. Keywords: Traffic sign, Segmentation, Gabor filter, Traffic Sign Detection and Recognition (TSDR)

Traffic Sign Detection Based on Convolutional Neural Network

Abstract: In today's world, nearly everything we have a tendency to do has been simplified by machine-driven tasks. In a trial to specialize in the road whereas driving, drivers usually miss out on signs on the facet of the road, that can be dangerous for them and for the folks around them. This drawback may be avoided if there was AN economical thanks to inform the motive force while not having them to shift their focus. Traffic Sign Detection and Recognition (TSDR) plays a vital role here by detection and recognizing a symptom, therefore notifying the motive force of any coming signs. This not solely ensures road safety, however additionally permits the motive force to be at very little a lot of ease whereas driving on tough or new roads. Another normally long-faced drawback isn't having the ability to know the which means of the sign. With the assistance of this Advanced Driver help Systems (ADAS) application, drivers can not face the matter of understanding what the sign says. during this paper, we have a tendency to propose a way for Traffic Sign Detection and Recognition exploitation image process for the detection of a symptom and a Convolutional Neural Networks (CNN) for the popularity of the sign. CNNs have a high recognition rate, therefore creating it fascinating to use for implementing varied laptop vision tasks. TensorFlow is employed for the implementation of the CNN. Keywords: actitvity recognition; knowledge collection; knowledge preprocessing; coaching CNN model ;evaluating model; predicting the result.

Traffic sign detection and recognition using RGSM and a novel feature extraction method

Independent mobility involves various challenges to Visual Impairment or Blindness (VIB) people. Most of the mobile devices are accessible to people with VIB that makes the use of available specific applications in online stores. Some applications support the independent mobility for VIB users in safely crossing road. The traffic sign detection and recognition (TSDR) is an essential challenge to VIB people. Existing research offers various techniques to detect the traffic sign in an open road environment. However, this system did not correctly recognizes the traffic sign. This research addressed the problem of traffic sign recognition to support the VIB people for safely crossing the road. Traffic sign detection and recognition are achieved by using novel Random Gradient Succession with Momentum (RGSM) with novel shape specific feature extraction method. Finally, the CNN classifier will be utilized to categorize the trained output labels, which then converts the traffic sign into the audio signal in both the training phase and the testing phase. The results are estimated for the performance measures like accuracy, specificity, precision, F-score, Jaccard coefficient, kappa, and Dice coefficient. Estimation of the results shows a better improvement for this parameter on comparing the proposed system with that of the existing methods. The proposed traffic sign detection system involves the robust audio signal processing that increased the feature extraction and classification performances. The suggested solution solves the obstacles faced by visually impaired peoples for independent mobility.

DFR-TSD: A Deep Learning Based Framework for Robust Traffic Sign Detection Under Challenging Weather Conditions

Robust traffic sign detection and recognition (TSDR) is of paramount importance for the successful realization of autonomous vehicle technology. The importance of this task has led to vast amount of research efforts and many promising methods have been proposed in the existing literature. However, most of these methods have been evaluated on clean and challenge-free datasets and overlooked the performance deterioration associated with different challenging conditions (CCs) that obscure the traffic-sign images captured in the wild. In this paper, we look at the TSDR problem under CCs and focus on the performance degradation associated with them. To this end, we propose a Convolutional Neural Network (CNN) based prior enhancement focused TSDR framework. Our modular approach consists of a CNN-based challenge classifier, Enhance-Net–an encoder-decoder CNN architecture for image enhancement, and two separate CNN architectures for sign-detection and classification. We propose a novel training pipeline for Enhance-Net that focuses on the enhancement of the traffic sign regions (instead of the whole image) in the challenging images subject to their accurate detection. We used CURE-TSD dataset consisting of traffic videos captured under different CCs to evaluate the efficacy of our approach. We experimentally show that our method obtains an overall precision and recall of 91.1% and 70.71% that is 7.58% and 35.90% improvement in precision and recall, respectively, compared to the current benchmark. Furthermore, we compare our approach with different CNN-based TSDR methods and show that our approach outperforms them by a large margin.

Traffic Sign Detection for Intelligent Transportation Systems: A Survey

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.

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges.

The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system.

A Survey on Traffic Sign Detection Techniques Using Text Mining

Traffic Sign Detection and Recognition (TSDR) technique is a critical step for ensuring vehicle safety. This paper provides a comprehensive survey on traffic sign detection and recognition system based on image and video data. The main focus is to present the current trends and challenges in the field of developing an efficient TSDR system. The ultimate aim of this survey is to analyze the various techniques for detecting traffic signs in real time applications. Image processing is a prominent research area, where multiple technologies are associated to convert an image into digital form and perform some functions on it, in order to get an enhanced image or to extract some useful information from it.