Different Types Of Traffic Signs Research Articles

TRAFFIC SIGN RECOGNITION WITH CONVOLUTIONAL NEURAL NETWORK

Recognizing road signs is one of the most important steps drivers can take to help prevent accidents. The purpose of the research work is to develop a recognition system, increasing the classification accuracy of the model, using deep learning methods of the road sign recognition system for drivers in real time on the road. Stages of road sign image classification were carried out, and other authors' solutions were analyzed. In addition, in this work, a convolutional neural network (CNN) was used for an autonomous traffic and road sign detection and recognition system. The proposed system works in real-time on the recognition of road signs images. In this paper, a model is trained using deep learning of 43 different road signs using existing datasets and collected local road signs. A traffic sign detection and recognition system is presented using an 8-layer convolutional neural network, which acquires different functions by training different types of traffic signs.
 In previous studies, models were trained using simple machine learning algorithms, but the relevance of this study is that a CNN model was trained for a classification task based on convolutional neural networks using deep learning. As a result of the study, classification accuracy of 95% was obtained using deep learning methods. As a novelty of the work, it is possible to note the diversity of the convolutional network methods used to increase the efficiency of the used data set and model training algorithms, the variety of received road signs and algorithms for its recognition, as well as the achievement of a high accuracy rate. This allowed the system to overcome the limited accuracy and performance issues caused by environmental factors, and to be more versatile and accurate than most modern systems.

Traffic Sign Classification Using Transfer Learning: An Investigation of Feature-Combining Model

The traffic sign classification system is a technology to help drivers to recognise the traffic sign hence reducing the accident. Many types of learning models have been applied to this technology recently. However, the deployment of learning models is unknown and shown to be non-trivial towards image classification and object detection. The implementation of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features as well as can save lots of training time. Besides, the feature-combining model exhibited great performance in the TL method in many applications. Nonetheless, the utilisation of such methods towards traffic sign classification applications are not yet being evaluated. The present study aims to exploit and investigate the effectiveness of transfer learning feature-combining models, particularly to classify traffic signs. The images were gathered from GTSRB dataset which consists of 10 different types of traffic signs i.e. warning, stop, repair, not enter, traffic light, turn right, speed limit (80km/s), speed limit (50km/s), speed limit (60km/s), and turn left sign board. A total of 7000 images were then split to 70:30 for train and test ratio using a stratified method. The VGG16 and VGG19 TL-features models were used to combine with two classifiers, Random Forest (RF) and Neural Network. In summary, six different pipelines were trained and tested. From the results obtained, the best pipeline was VGG16+VGG19 with RF classifier, which was able to yield an average classification accuracy of 0.9838. The findings showed that the feature-combining model successfully classifies the traffic signs much better than the single TL-feature model. The investigation would be useful for traffic signs classification applications i.e. for ADAS systems

High-performance Chinese multiclass traffic sign detection via coarse-to-fine cascade and parallel support vector machine detectors

The high variability of sign colors and shapes in uncontrolled environments has made the detection of traffic signs a challenging problem in computer vision. We propose a traffic sign detection (TSD) method based on coarse-to-fine cascade and parallel support vector machine (SVM) detectors to detect Chinese warning and danger traffic signs. First, a region of interest (ROI) extraction method is proposed to extract ROIs using color contrast features in local regions. The ROI extraction can reduce scanning regions and save detection time. For multiclass TSD, we propose a structure that combines a coarse-to-fine cascaded tree with a parallel structure of histogram of oriented gradients (HOG) + SVM detectors. The cascaded tree is designed to detect different types of traffic signs in a coarse-to-fine process. The parallel HOG + SVM detectors are designed to do fine detection of different types of traffic signs. The experiments demonstrate the proposed TSD method can rapidly detect multiclass traffic signs with different colors and shapes in high accuracy.

Fast Traffic Sign Recognition via High-Contrast Region Extraction and Extended Sparse Representation

In this paper, we propose a high-performance traffic sign recognition (TSR) framework to rapidly detect and recognize multiclass traffic signs in high-resolution images. This framework includes three parts: a novel region-of-interest (ROI) extraction method called the high-contrast region extraction (HCRE), the split-flow cascade tree detector (SFC-tree detector), and a rapid occlusion-robust traffic sign classification method based on the extended sparse representation classification (ESRC). Unlike the color-thresholding or extreme region extraction methods used by previous ROI methods, the ROI extraction method of the HCRE is designed to extract ROI with high local contrast, which can keep a good balance of the detection rate and the extraction rate. The SFC-tree detector can detect a large number of different types of traffic signs in high-resolution images quickly. The traffic sign classification method based on the ESRC is designed to classify traffic signs with partial occlusion. Instead of solving the sparse representation problem using an overcomplete dictionary, the classification method based on the ESRC utilizes a content dictionary and an occlusion dictionary to sparsely represent traffic signs, which can largely reduce the dictionary size in the occlusion-robust dictionaries and achieve high accuracy. The experiments demonstrate the advantage of the proposed approach, and our TSR framework can rapidly detect and recognize multiclass traffic signs with high accuracy.

Detection, classification, and mapping of U.S. traffic signs using google street view images for roadway inventory management

Abstract Background Maintaining an up-to-date record of the number, type, location, and condition of high-quantity low-cost roadway assets such as traffic signs is critical to transportation inventory management systems. While, databases such as Google Street View contain street-level images of all traffic signs and are updated regularly, their potential for creating an inventory databases has not been fully explored. The key benefit of such databases is that once traffic signs are detected, their geographic coordinates can also be derived and visualized within the same platform. Methods By leveraging Google Street View images, this paper presents a new system for creating inventories of traffic signs. Using computer vision method, traffic signs are detected and classified into four categories of regulatory, warning, stop, and yield signs by processing images extracted from Google Street View API. Considering the discriminative classification scores from all images that see a sign, the most probable location of each traffic sign is derived and shown on the Google Maps using a dynamic heat map. A data card containing information about location and type of each detected traffic sign is also created. Finally, several data mining interfaces are introduced that allow for better management of the traffic sign inventories. Results The experiments conducted on 6.2 miles of I-57 and I-74 interstate highways in the U.S. –with an average accuracy of 94.63 % for sign classification– show the potential of the method to provide quick, inexpensive, and automatic access to asset inventory information. Conclusions Given the reliability in performance shown through experiments and because collecting information from Google Street View imagery is cost-effective, the proposed method has potential to deliver inventory information on traffic signs in a timely fashion and tie into the existing DOT inventory management systems. Such spatio-temporal representations provide DOTs with information on how different types of traffic signs degrade over time and further provides useful condition information necessary for predicting sign replacement plan.

Rapid Multiclass Traffic Sign Detection in High-Resolution Images

This paper describes a traffic sign detection (TSD) framework that is capable of rapidly detecting multiclass traffic signs in high-resolution images while achieving a high detection rate. There are three key contributions. The first is the introduction of two features called multiblock normalization local binary pattern (MN-LBP) and tilted MN-LBP (TMN-LBP), which are able to express multiclass traffic signs effectively. The second is a tree structure called split-flow cascade, which utilizes common features of multiclass traffic signs to construct a coarse-to-fine TSD detector. The third contribution is the Common-Finder AdaBoost (CF.AdaBoost) algorithm, which is designed to find common features of different training sets to develop an efficient Split-Flow Cascade tree (SFC-tree) for multiclass TSD. Through experiments with an evaluation data set of high-resolution images, we show that the proposed framework is able to detect multiclass traffic signs with high detection accuracy in real time and that it outperforms the state-of-the-art approaches at detecting a large number of different types of traffic signs rapidly without using any color information.

Traffic Sign Recognition Using Sparse Representations and Active Contour Models

Automated traffic sign recognition (TSR) remains an integral component of road sign inventory programs administered by transportation agencies. However, current practices for TSR are labor-intensive and time-consuming. This paper presents a novel TSR algorithm that can be integrated into the traffic sign management systems of infrastructure managers. The methodology proposed the use of a multiscale principal component pursuit (mPCP) for detection of the locations of traffic signs that decomposes each video frame into a background image (trees, cars, illumination, etc.) and foreground image (traffic sign information). Next, a novel model method for edge detection called “active contours,” or “snakes,” was introduced to extract traffic sign geometries for classification. The active contour models showed promise for the detection of different types of traffic signs in noisy environments. The ability of these models to split and match topologies of the image data was essential for accurate sign location and shape detection. The evaluation of the proposed methodology was conducted with data from video logs. The performance of the system developed was compared with that of the Canny- and Hough-based TSR algorithms. The test results indicated that the mPCP–active contour-based TSR algorithm remarkably outperformed the selected benchmark algorithms, with the highest detection rate (92.6%) and precision (87.2%). These results demonstrated the potential for practical application of the proposed methodology.

공사구간 형광주황색 교통안전표지 적용에 따른 주목성 효과 연구

본 논문은 공사구간에서 사용되고 있는 교통안전표지의 생상과 관련된 문제점 분석 및 해외사례 검토를 바탕으로 공사구간에서 운전자들의 보다 안전한 주행을 도모하기 위해 공사구간 내의 교통안전표지에 형광 주황색을 적용하는 것에 대한 타당성을 평가하고자 한다. 이를 위해, 먼저 현재 공사구간에서 사용되고 있는 교통안전표지의 근본적인 형상과 색상을 검토하였다. 또한 형광 주황색을 이용하여 공사구간 교통안내시설을 제작한 후 실내 육안 평가 및 주행 시뮬레이터를 이용해 형광 주황색 사용의 효과를 측정하였다. 실내 육안 평가 결과, 현재 고속도로에서 사용 중인 황색 바탕의 흑색 글씨 또는 일반국도 등에서 사용 중인 백색 바탕의 적색 글씨로 제작된 공사구간 표지와 비교하였을 때 형광 주황색 바탕에 흑색 글씨로 제작된 안내표지, 예고표지, 갈매기표지 그리고 유도표지가 주목성 측면에서 모두 70% 이상의 선호도를 보이고 있는 것을 확인하였다. 또한 주행 시뮬레이터를 이용한 실험에서는 본 연구에서 제안하는 형광 주황색 바탕, 고속도로에서 사용 중인 황색 바탕, 그리고 일반국도 등에서 사용 중인 백색 바탕의 공사구간 안내표지를 비교하였다. 비교 결과 형광 주황색 바탕의 안내표지가 황색 바탕 안내표지 보다는 15m 전방에서, 그리고 백색 바탕 안내표지보다는 25m 전방에서 인지됨을 확인하였다. This study intended to improve traffic safety in work zones using fluorescent orange color traffic signs. For this end, the current problems of existing traffic signs and facilities in work zones were analyzed and some good examples of foreign countries were compared. More specifically, in order to identify current problems, the basic shape and color of traffic signs in use were examined through literature review, surveys and field studies. It was found that the fluorescent color worked more effectively than other colors in terms of visibility and conspicuity in work zone sign system. For evaluation, both Conspicuity surveys and in-door simulator experiments were conducted to effectively assess the merits and demerits of different types of traffic signs for work zones. The results of evaluations showed that black lettering on a fluorescent orange background outperformed more than 70% in terms of visibility and ability to call drivers' attention compared with those from black/red lettering on yellow/white backgrounds currently used at work zones on expressways and national highways. In addition, simulated driving experiment disclosed that drivers recognized the fluorescent orange background sign 15m ahead compared with the yellow background sign and 25m ahead compared with the white background sign, respectively. As for the diamond-shaped "Under Construction" sign, drivers recognized fluorescent orange background 11m ahead compared with the yellow background sign and 19m ahead compared with the white background sign, respectively.