Automatic Traffic Sign Detection Research Articles

Intelligent Traffic Sign Detection Using Yolov9

This research examines the automatic detection and classification of traffic signs using artificial intelligence (AI) and computer vision technologies. As urban traffic increases, quickly and accurately recognizing traffic signs becomes a challenge, especially under adverse conditions such as bad weather and limited visibility. Conventional technologies that rely on human vision are prone to errors, so an automated solution is needed. This research uses the YOLOv9 algorithm for real-time traffic sign detection, utilizing the Generalized ELAN (GELAN) architecture that combines the advantages of CSPNet and ELAN for efficiency and accuracy. The dataset used consists of 1924 images processed through various stages, including data augmentation and normalization. The model was trained for 15 epochs with fairly high accuracy results in the prohibitory, danger, and mandatory sign categories. However, there were still some misclassifications, especially in the prohibitory category which was sometimes mistakenly detected as another category or background. Overall, the model performed well in detecting traffic signs in various environmental conditions, but still needs improvement to increase accuracy in certain cases.

Indian traffic sign detection and recognition using deep learning

Traffic signs are a fundamental piece of transportation infrastructure and play a vital role in regulating traffic flow, enforcing proper driving behavior, and reducing the risk of accidents, injuries, and fatalities. An Intelligent Transportation System (ITS) must have the ability to automatically detect the sign and then recognise traffic signs which is to be effective. Automatic traffic sign detection is necessary. and is growing in significance with the advent of self-driving cars. This study introduces a brand-new deep learning-based method for identifying traffic signs in India. The proposed system utilizes a region-based convolutional neural network (CNN) to achieve automatic identification and recognition of traffic signs. The authors describe various architectural and data augmentation enhancements to the CNN model and take into account unique and challenging Indian traffic sign types that have not been previously discussed in literature. The system is trained and evaluated using a database of real-time images captured on Indian highways. The deep learning approach is utilized to work on the accuracy and precision of the system, determined to make automated driving automobiles.

Indian traffic sign detection and recognition using deep learning

Traffic signs play a crucial role in managing traffic on the road, disciplining the drivers, thereby preventing injury, property damage, and fatalities. Traffic sign management with automatic detection and recognition is very much part of any Intelligent Transportation System (ITS). In this era of self-driving vehicles, calls for automatic detection and recognition of traffic signs cannot be overstated. This paper presents a deep-learning-based autonomous scheme for cognizance of traffic signs in India. The automatic traffic sign detection and recognition was conceived on a Convolutional Neural Network (CNN)- Refined Mask R-CNN (RM R-CNN)-based end-to-end learning. The proffered concept was appraised via an innovative dataset comprised of 6480 images that constituted 7056 instances of Indian traffic signs grouped into 87 categories. We present several refinements to the Mask R-CNN model both in architecture and data augmentation. We have considered highly challenging Indian traffic sign categories which are not yet reported in previous works. The dataset for training and testing of the proposed model is obtained by capturing images in real-time on Indian roads. The evaluation results indicate lower than 3% error. Furthermore, RM R-CNN’s performance was compared with the conventional deep neural network architectures such as Fast R-CNN and Mask R-CNN. Our proposed model achieved precision of 97.08% which is higher than precision obtained by Mask R-CNN and Faster R-CNN models.

Automatic Traffic Sign Detection and Classification

Abstract: Automatic detection and recognition of road traffic signs is an essential task for regulating the traffic, guiding, and warning the drivers and pedestrians. M a n y challenges like cluttered background, foreground scenery, various geographic, metrological, weather conditions namely cloudy day, rain, snow, fog, changeable and uncontrollable lighting conditions depending on the time of the day exhibits the detection of road signs. The traffic sign may have different sizes and colors. Our goal is to detect the traffic signs with red, blue, yellow colors with any of the available shapes. Then the detected signs, combined with the color and shape information are classified. The recognition of Traffic sign involves two stages: detection stage, it finds the region consisting of traffic signs from the image and then the classification stage where the detected signs are categorized into different classes like information, warning, prohibition and so on. The image is acquired and is processed in CSR block, in which the base features from the image are extricated. The base features extricated from the image are color, shape, and Region of Interest (ROI) position. This pre-processing is very quick, because no special transformation is required. RGB and HSV color space is chosen for color segmentation. By extracting the color, CSR block creates 3 binary maps (red, blue, and yellow). From these binary maps, the shape of the traffic sign is detected. From the image the ROI is segmented, which is the input to the shape detecting block. After the shape is detected, traffic sign is classified according to its detected color and shape.

SC-YOLO: A Object Detection Model for Small Traffic Signs

Automatic traffic sign detection has great potential for intelligent vehicles. In recent years, traffic sign detection has made significant progress with the rise of deep learning. Detecting small traffic signs in real-world scenarios is still a challenging problem due to the complex and variable traffic environment. In this paper, a model with a small number of parameters is proposed to improve the accuracy of small traffic sign detection. Firstly, the cross-stage attention network module is proposed to enhance the feature extraction capability of the network. Secondly, a dense neck structure is proposed to make the detail information and semantic information fully fused. Finally, for the model’s loss function, SIOU with direction information is introduced to optimize the model’s training process. Tests on the challenging public datasets TT100K, CCTSDB2021, and VOC show that our approach achieves significant performance improvement with the minimum number of parameters compared to existing algorithms.

Deep-learning-based Automatic Detection and Classification of Traffic Signs Using Images Collected by Mobile Mapping Systems

Deep-learning-based Automatic Detection and Classification of Traffic Signs Using Images Collected by Mobile Mapping Systems

Traffic Sign Detection and Recognition in Multiimages Using a Fusion Model With YOLO and VGG Network

The detection and recognition of traffic signs is an important topic in intelligent transportation systems. The automatic detection and recognition of traffic signs during driving is the basis for realizing the unmanned driving. Therefore, the work on the detection and recognition of traffic signs has a potential value and application prospect. In the traditional detection and recognition methods, they often detect and recognize traffic signs image by image. In this case, only the information of the current image is used, and the relationship between the image sequences is not considered. To end this issue, we propose a novel model that can use the relationship in multi-images to detect and recognize traffic signs in a driving video sequence quickly and accurately. The model proposed in this paper is a fusion model based on YOLO-V3 and VGG19 network. Finally, we test this proposed model on a public dataset and compare it to the baseline method, and results show that this proposed model achieves accuracy over 90% and outperforms the baseline method for all types of traffic signs in different conditions. Thus, we can conclude this proposed model is efficient and accurate.

Mexican traffic sign detection and classification using deep learning

Automatic detection and classification of traffic signs is challenging to support a driver’s safety and even assist in autonomous driving. This paper aims to propose a methodology for detecting and classifying Mexican traffic signs using deep learning. The methodology consists of the creation of a new Mexican traffic sign data set, the training, testing, and comparing of two sign detectors (the Region-based Convolutional Neural Network (R-CNN) and the You Only Look Once (YOLO v3)), and the use of a modified Residual Neural Network (ResNet-50) for classification. According to the detection results, the combination R-CNN/ResNet-50 yielded a mean Average Precision (mAP) of 95.33%, while the YOLO v3/ResNet-50 yielded 90.33%. The overall classification accuracy was 99.00%. Our results are competitive to those presented in the literature. We demonstrated the robustness of our proposal by conducting a test to classify images that do not contain traffic signs. The accuracy for the R-CNN/ResNet-50 was 99.5% and 99.77% for the YOLO v3/ResNet-50. We also obtained satisfactory classification results with traffic signs occluded and inserted in random positions in the scene. Finally, an ablation study regarding the data set and the batch size was conducted.

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.

Hybrid Framework on Automatic Detection and Recognition of Traffic Display board Signs

Automatically identifying traffic signs is a challenging and time-consuming process. As the academic community pays more attention to traditional algorithms for vision-based detection, tracking, and classification, three main criteria drive the investigation, they are detection, tracking, and classification. It is capable of performing detection and identification operations to minimize traffic accidents and move towards autonomous cars. A novel method proposed in this paper is based on moment invariants and neural networks for performing detection and recognition with classification, and it also includes automatic detection and identification of traffic signs and traffic board text that uses colour segmentation. Aside from the proposed structure, it is also required to identify the potential graphic road marking with text. This research article contains two algorithms, which are used to accurately classify the board text. The detection through image segmentation and recognition can be done by using the CNN algorithm. Finally, the classification is performed by the SVM framework. Therefore, the proposed framework will be very accurate and reliable with high efficiency, which has been proven in many big dataset applications. The proposed algorithm is tested with various datasets and provided good identification rate compared to the traditional algorithm.

Using geometric constraints to improve performance of image classifiers for automatic segmentation of traffic signs

Automatic detection and recognition of traffic signs from images is an important topic in many applications. At first, we segmented the images using a classification algorithm to delineate the areas where the signs are more likely to be found. In this regard, shadows, objects having similar colours, and extreme illumination changes can significantly affect the segmentation results. We propose a new shape-based algorithm to improve the accuracy of the segmentation. The algorithm works by incorporating the sign geometry to filter out the wrong pixels from the classification results. We performed several tests to compare the performance of our algorithm against those obtained by popular techniques such as Support Vector Machine (SVM), K-Means, and K-Nearest Neighbours. In these tests, to overcome the unwanted illumination effects, the images are transformed into colour spaces Hue, Saturation, and Intensity, YUV, normalized red green blue, and Gaussian. Among the traditional techniques used in this study, the best results were obtained with SVM applied to the images transformed into the Gaussian colour space. The comparison results also suggested that by adding the geometric constraints proposed in this study, the quality of sign image segmentation is improved by 10%–25%. We also comparted the SVM classifier enhanced by incorporating the geometry of signs with a U-Shaped deep learning algorithm. Results suggested the performance of both techniques is very close. Perhaps the deep learning results could be improved if a more comprehensive data set is provided.

A Traffic-Sign Detection Algorithm Based on Improved Sparse R-cnn

Automatic traffic-sign detection is a hot topic in computer vision and one of the critical technologies of intelligent transportation. The Transformer structure has recently become a research hotspot due to its excellent performance. We hope to apply this structure to the design of traffic sign detection algorithms. Therefore, we make some improvements to Sparse R-cnn, a neural network model inspired by Transformer. Sparse R-cnn is a novel model, and its core idea is to replace hundreds of thousands of candidate anchors in the RPN network with a small set of proposal boxes. The experiments in our paper proved that the performance of the Sparse R-cnn model is better than other existing general object detection models. Based on the original Sparse R-cnn inspiration, an improved Sparse R-cnn model is proposed. First, a novel backbone for the task of traffic-sign detection is proposed. Multi-scale fusion structure is the essential method of improving the algorithm for small target detection, so improving the multi-scale capability of the backbone is a required method for designing traffic sign detection. So, we made further improvements to the existing backbone ResNest. We enhanced the multi-scale representation ability of the backbone by constructing hierarchical residual-like connections within each single radix block in the original ResNest. We call the improved backbone Res2Nest. The novel backbone proposed by us shows better performance without introducing excessive computational costs to the model. In addition, the attention mechanism is also an effective method to improve the detection of traffic signs, so we set up a branch network for recalibrating the channel feature response adaptively through the Global Average Pooling (GAP) operation and a fully connected layer. It can also be seen as the implementation of the cross-channel self-attention mechanism. After experiments by TT100K dataset, our method would attain a better accuracy and robustness.

Improving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation

Automatic detection and recognition of traffic signs have many applications. However, some problems can affect the accuracy of the existing algorithms, such as changes in environmental light conditions, shadows, the presence of objects of the same colour, significant changes in scale and rotation, as well as obstacles in front of the traffic signs. To overcome these difficulties, a reference image database is usually used that includes different modes of appearing the traffic signs in the images. In order to overcome the effects of scale and rotation, in this paper a new method is presented in which only one reference image is needed for each sign to recognise the traffic sign in an image. In the proposed method, imaging is done in stereo. Using the captured image pair, a virtual image is generated which is then used to recognise the sign. As a result, the recognition is carried out with a minimum number of reference images. Experiments show that the proposed algorithm significantly improves recognition results. The traffic signs are recognised with 93.1% accuracy that enjoys a 4.9% improvement over traditional methods.

Detecting Text in the Scene Text Image Using Fast Fourier Transform

Text in images express a lot of information for image understanding. Detecting and recognizing text facilitates many important applications. The characteristics of a text with different shapes and contour patterns require special computer vision to detect and recognize when compared to other objects in the image. Text detection is the process of detecting text in an image followed by surrounding text with a rectangular bounding box. It can be performed by using image-based or frequency-based techniques. Many applications require text detection in images ranging from automatic detection of traffic signs that help in the transportation system to assist blind people and others in need. The data of this study were Focused Scene Text with the JPG format. The method used to detect the text in images was Fast Fourier Transform (FFT). The final results of the study showed that 95% of the text could be detected in an image by various camera perspectives and was expected to help develop text recognition applications in an image.

Automatic Detection of Regulatory Traffic Signs via Circle Detection by Post Edge Detection Applied to Straight Line Hough Transform

In this paper, a new effective method for automatic detection of circular traffic signs is introduced. Automatic traffic sign recognition is a crucial application of Driver Assistance Systems for safe and comfortable driving conditions. The major step of traffic sign recognition is to detect and localize the traffic signs if they exist. An important portion of traffic signs (i.e. regulatory traffic signs) has circular shape. They include vital information about the traffic rules and regulations (especially the speed limits). Therefore, this study introduces an advanced circular detection method to detect and localize the circular traffic signs. In the previous literature, it is known that a circle creates a distinctive sinusoidal structure in Straight Line Hough Transform (SLHT). This study exploits this notion in circle detection by trying to catch a part of the envelopes of this sinusoidal structure. First, post edge detection is applied to SLHT, and this image is called H-Edge image. Then, edge linking is performed on H-Edge image to obtain multiple candidate curves. By sinusoid curve fitting and sinusoidal normalization, the curves belonging to the sinusoidal structure are identified, so the circle in the original image is detected. Furthermore, a new effective iterative linear image segmentation method which is based on local minima in SLHT is proposed. Combining these two methods and color filtering, a new effective method for circular traffic sign detection is obtained. For certain sample images, the new method effectively detects and localizes the existing circular traffic signs.

Deep Learning for Large-Scale Traffic-Sign Detection and Recognition

Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory. It provides an accurate and timely way to manage traffic-sign inventory with a minimal human effort. In the computer vision community, the recognition and detection of traffic signs are a well-researched problem. A vast majority of existing approaches perform well on traffic signs needed for advanced driver-assistance and autonomous systems. However, this represents a relatively small number of all traffic signs (around 50 categories out of several hundred) and performance on the remaining set of traffic signs, which are required to eliminate the manual labor in traffic-sign inventory management, remains an open question. In this paper, we address the issue of detecting and recognizing a large number of traffic-sign categories suitable for automating traffic-sign inventory management. We adopt a convolutional neural network (CNN) approach, the mask R-CNN, to address the full pipeline of detection and recognition with automatic end-to-end learning. We propose several improvements that are evaluated on the detection of traffic signs and result in an improved overall performance. This approach is applied to detection of 200 traffic-sign categories represented in our novel dataset. The results are reported on highly challenging traffic-sign categories that have not yet been considered in previous works. We provide comprehensive analysis of the deep learning method for the detection of traffic signs with a large intra-category appearance variation and show below 3% error rates with the proposed approach, which is sufficient for deployment in practical applications of the traffic-sign inventory management.

Small traffic sign detection from large image

Automatic traffic sign detection has great potential for intelligent vehicles. The ability to detect small traffic signs in large traffic scenes enhances the safety of intelligent devices. However, small object detection is a challenging problem in computer vision; the main problem involved in accurate traffic sign detection is the small size of the signs. In this paper, we present a deconvolution region-based convolutional neural network (DR-CNN) to cope with this problem. This method first adds a deconvolution layer and a normalization layer to the output of the convolution layer. It concatenates the features of the different layers into a fused feature map to provide sufficient information for small traffic sign detection. To improve training effectiveness and distinguish hard negative samples from easy positive ones, we propose a two-stage adaptive classification loss function for region proposal networks (RPN) and fully connected neural networks within DR-CNN. Finally, we evaluate our proposed method on the new and challenging Tsinghua-Tencent 100K dataset. We further conduct ablation experiments and analyse the effectiveness of the fused feature map and the two-stage classification loss function. The final experimental results demonstrate the superiority of the proposed method for detecting small traffic signs.

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.

Automatic Traffic Sign Detection and Recognition Using SegU-Net and a Modified Tversky Loss Function With L1-Constraint

Traffic sign detection is a central part of autonomous vehicle technology. Recent advances in deep learning algorithms have motivated researchers to use neural networks to perform this task. In this paper, we look at traffic sign detection as an image segmentation problem and propose a deep convolutional neural network-based approach to solve it. To this end, we propose a new network, the SegU-Net, which we form by merging the state-of-the-art segmentation architectures–SegNet and U-Net to detect traffic signs from video sequences. For training the network, we use the Tversky loss function constrained by an L1 term instead of the intersection over union loss traditionally used to train segmentation networks. We use a separate network, inspired by the VGG-16 architecture, to classify the detected signs. The networks are trained on the challenge free sequences of the CURE-TSD dataset. Our proposed network outperforms the state-of-the-art object detection networks, such as the Faster R-CNN inception Resnet V2 and R-FCN Resnet 101, by a large margin and obtains a precision and recall of 94.60% and 80.21%, respectively, which is the current state of the art on this part of the dataset. In addition, the network is tested on the German Traffic Sign Detection Benchmark (GTSDB) dataset, where it achieves a precision and recall of 95.29% and 89.01%, respectively. This is on a par with the performance of the aforementioned object detection networks. These results prove the generalizability of the proposed architecture and its suitability for robust traffic sign detection in autonomous vehicles.

Improving the Classification Accuracy of Accurate Traffic Sign Detection and Recognition System Using HOG and LBP Features and PCA-Based Dimension Reduction

The automatic traffic sign detection and recognition (TSDR) is one of the most useful, important and consequential method towards designing automated and driverless vehicles. It is one of the real challenges to overcome. Even in current vehicles with drivers, it is an important matter which need to be tackled for enhancing driver’s safety. In this research, we propose to use histogram-oriented gradient (HOG) and local binary patterns (LBP) approaches for feature extraction followed by PCA-based dimension reduction for accurate TSDR system. The full approach and performance are measured on Chinese traffic sign (TS) database. The development approach of the system is divided into three steps: Image processing, detection, and recognition. For the detection of traffic signs, we used an RGB color segmentation (thresholding) technique with circular Hough Transform for circular TSDR and Hough transform Algorithm for shape detection. For recognition, features are extracted using techniques HOG and LBP. Further principal component analysis (PCA) is applied for strong component extraction and classification is performed using support vector machines (SVM) classifier. The result section validates the effectiveness of using the proposed features, as the classification accuracy has been improved with respect to previously reported works on TSDR.