Research on Traffic Sign Detection and Recognition System Using Deep Ensemble Learning

Automatic detection and recognition of traffic sign has been a topic of great interest in advanced driver assistance system. It enhances vehicle and driver safety by providing the condition and state of the road to the drivers. However, visual occlusion and ambiguities in the real-world scenario make the traffic sign recognition a challenging task. This paper presents an Automatic Traffic Sign Detection and Recognition (ATSDR) system, involving three modules: segmentation, detection, and recognition. Region of Interest (ROI) is extracted using multiple thresholding schemes with a novel environmental selection strategy. Then, the traffic sign detection is carried out using correlation computation between log-polar mapped inner regions and the reference template. Finally, recognition is performed using Support Vector Machine (SVM) classifier. Our proposed system achieved a recognition accuracy of 98.3 % and the experimental results demonstrates the robustness of traffic sign detection and recognition in real-world scenario.

Detection and recognition steps are the key elements of a full traffic sign system. In this chapter, a complete description of a complete traffic sign system and how the component pieces are glued together are provided. Starting with the stereo acquisition module, data are fed into an attentional cascade detection algorithm. This step is performed separately for each video source and the coherence of the detections for the stereo pair is checked. As a result the location of the traffic sign and its size if obtained. The detected signs are normalized taking into account their shape. Then, they are given to the Forest-ECOC classification stage. All stages are validated individually and as a whole providing competitive results with respect to the state-of-the-art techniques.

Traffic sign detection and recognition systems include a variety of applications like autonomous driving, road sign inventory, and driver support systems. Machine learning algorithms provide useful tools for traffic sign identification tasks. However, classification algorithms depend on the preprocessing stage to obtain high accuracy rates. This paper proposes a road sign characterization method based on oriented gradient maps and the Karhunen-Loeve transform in order to improve classification performance. Dimensionality reduction may be important for portable applications on resource constrained devices like FPGAs; therefore, our approach focuses on achieving a good classification accuracy by using a reduced amount of attributes compared to some state-of-the-art methods. The proposed method was tested using German Traffic Sign Recognition Benchmark, reaching a dimensionality reduction of 99.3% and a classification accuracy of 95.9% with a Multi-Layer Perceptron.

In this paper, we discuss theoretical foundations and a practical realization of a real-time traffic sign detection, tracking and recognition system operating on board of a vehicle. In the proposed framework, a generic detector refinement procedure based on mean shift clustering is introduced. This technique is shown to improve the detection accuracy and reduce the number of false positives for a broad class of object detectors for which a soft response’s confidence can be sensibly estimated. The track of an already established candidate is maintained over time using an instance-specific tracking function that encodes the relationship between a unique feature representation of the target object and the affine distortions it is subject to. We show that this function can be learned on-the-fly via regression from random transformations applied to the image of the object in known pose. Secondly, we demonstrate its capability of reconstructing the full-face view of a sign from substantial view angles. In the recognition stage, a concept of class similarity measure learned from image pairs is discussed and its realization using SimBoost, a novel version of AdaBoost algorithm, is analyzed. Suitability of the proposed method for solving multi-class traffic sign classification problems is shown experimentally for different feature representations of an image. Overall performance of our system is evaluated based on a prototype C++ implementation. Illustrative output generated by this demo application is provided as a supplementary material attached to this paper.

Traffic Sign Recognition (TSR) system is a significant component of Intelligent Transport System (ITS) as traffic signs assist the drivers to drive more safely and efficiently. This paper represents a new approach for TSR system where detection of traffic sign is carried out using fuzzy rules based color segmentation method and recognition is accomplished using Speeded Up Robust Features(SURF) descriptor, trained by artificial neural network (ANN) classifier. In the detection step, the region of interest (sign area) is segmented using a set of fuzzy rules depending on the hue and saturation values of each pixel in the HSV color space, post processed to filter unwanted region. Finally the recognition of the traffic sign is implemented using ANN classifier upon the training of SURF features descriptor. The proposed system simulated on offline road scene images captured under different illumination conditions. The detection algorithm shows a high robustness and the recognition rate is quite satisfactory. The performance of the ANN model is illustrated in terms of cross entropy, confusion matrix and receiver operating characteristic (ROC) curves. Also, performances of some classifier such as Support Vector Machine (SVM), Decision Trees, Ensembles Learners (Adaboost) and K-Nearest Neighbor (KNN) classifier are assessed with ANN approach. The simulation results illustrate that recognition using ANN model is higher than classifiers stated above.

Locating roadworks sites via detecting change in lateral positions of traffic signs measured relative to the ego-car

Traffic sign recognition system is a great important part of intelligent transportation system and advanced auxiliary driving system, and it is a key problem to improve the accuracy and real-time performance of traffic sign detection in reality.Considering to the perspective of accuracy and real-time of traffic sign detection and recognition, this article built the traffic sign detection and recognition method based on MATLAB. Finally, the paper proved the conclusion, and future traffic sign detection and recognition need to be further research topics and practical application prospect.

Traffic Sign Recognition (TSR) system is a vital component of intelligent transport system. It plays an important role by enhancing the safety of the drivers, pedestrians and vehicles as traffic signs provide important information of the traffic environment of the road and assist the drivers to drive more safely and easily by guiding and warning. This paper represents road sign detection and recognition system based on speeded up robust feature (SURF) descriptor that is invariant to rotation, skew and occlusion of the sign and artificial neural network (ANN) classifier. The proposed system consists of three steps. In the first step the road scene image is segmented using color based thresholding algorithm followed by some post processing like morphological analysis and filtering using region properties to extract the road sign as a blob of region. Next, shape verification is carried out by using translation, scaling and rotation invariant features vector named Distance to Borders (DtBs) of the segmented blob. Finally the recognition of the traffic sign is implemented using ANN classifier with the training of SURF descriptor. The proposed system simulated on offline road scene images shows a high success rate in the recognition stage. The performance of the classifier is illustrated in terms of cross entropy, confusion matrix and receiver operating characteristic (ROC) curve.

Traffic sign detection plays an important role in a traffic sign recognition system. It is also an unsolved problem in the intelligence transportation system. Two main technique routes are typically used in current research of the traffic sign detection: the technique using gray-level object image or that using color object image. The former has the advantage that the related theories and methods are more mature, but the weakness is short of information, which results in more chances of misdetection. The latter has the advantage that more abundant information can be provided same as human sense of vision, but the weakness is that the related theories and methods are still immaturity. This research proposes a concept of color-shape pair based on the prior characteristic of the color and the geometric shape by analyzing the traffic signs that might appear in Chinese highways. Also, a model of traffic signs, color-geometric model, is given according to the relative definition of the Color-Shape Pair. This model is constituted with 5 kinds of basic shapes and 3 kinds of basic colors of the traffic signs, focusing on the unique determinacy that is the most important characteristics of color and geometric shape. Therefore, using the detection of traffic signs based on color-geometric model, we can realize the rough classification of the traffic signs. Furthermore, we can divide 116 kinds of traffic signs on Chinese road into 7 subclasses, and reduce the complexity of recognition system of road traffic signs. The simulation experiment results have shown that this method can achieve 100% correct ratio for the traffic sign detection and the rough classification, and the robustness and real-time performance are also improved.

Traffic sign recognition systems (TSR) are a hot research topic because of their importance in the field of driver safety. Major car companies are developing smart driving systems for this purpose. TSR was introduced in 2022 in some luxury cars, such as the Cadillac XT4 and Mercedes-Benz GLS. TSR is a driver assistance feature that can be used to advise and warn the driver about possible road limitations along the current route. The development of the TSR system aims at two objectives; the first is to develop a fast and inexpensive system that can be added to economic vehicles. The second objective is to apply safe leadership automatically when accessing a fast and reliable TSR system. Following laws can be made compulsory by the vehicle itself, such as not exceeding speed in the event of a speed limit. (except in emergencies).There are many issues and challenges facing the researcher that works on the detection and recognition of traffic signs, such as bad weather, various illumination, occlusion, spped of cars.This research paper aims to present a proposal that facilitates access to a fast, reliable, and low-cost system. The proposed TSR is based on combining the You Only Lock Once (YOLOv5) network with a convolution neural network (CNN) that enhances the accuracy of detection and recognition in addition to speeding up the processing. Two datasets were used for training models. The YOLOv5 was trained on the GTSDB, while CNN was trained on the GTSRB.YOLOv5 is used to detect the traffic signs in the input image. The detected signs are bounded by boxes and labeled according to the group that belongs to them (the dataset is clustered into four groups (prohibitory, mandatory, danger, and others)), and then each bounded box is segmented from the rest of the image background. The segmented image (including the traffic sign) is input to the CNN to be classified into one of the 43 traffic signs.This proposed system achieved an average of 94% detection accuracy by using YOLO and 99.95% classification accuracy in CNN, with a processing time of 0.031 seconds per frame. In real-time applications, this low computation time can improve the system's stability and dependability.

The Traffic Sign Recognition (TSR) system is an essential part of traffic management, being support system for driver and intelligent vehicle. Traffic Sign Detection (TSD) is the prerequisite step of the automatic TSR system. This paper proposes a TSD framework by exploring fuzzy image processing and invariant geometric moments. In this framework, fuzzy inference system is applied to convert the HSV image into gray tone. Then, statistical threshold is used for the segmentation. After shape verification of every connected component using Hu moments and Quadratic Discriminant Analysis (QDA) model, the candidate signs are detected by referencing bounding box parameters. The framework is simulated in different complex scenarios of day and night mode. Experimental result shows that its performance is satisfactory and recommendable with the state of the art research. The proposed framework yields 94.86% F-measure in case of Bangladesh road signs and 93.01% in German road signs.

Traffic sign recognition is an important step for integrating smart vehicles into existing road transportation systems. In this paper, an NVIDIA Jetson TX1-based traffic sign recognition system is introduced for driver assistance applications. The system incorporates two major operations, traffic sign detection and recognition. Image color and shape based detection is used to locate potential signs in each frame. A pre-trained convolutional neural network performs classification on these potential sign candidates. The proposed system is implemented on NVIDIA Jetson TX1 board with web-camera. Based on a well-known benchmark suite, 96% detection accuracy is achieved while executing at 1.6 frames per seconds.

In this paper, an FPGA-based traffic sign recognition system is introduced for driver assistance applications. The system incorporates two major operations, traffic sign detection and recognition. The algorithms presented include hue detection for potential sign detection, morphological filters for noise reduction, labeling and Hausdorff distance calculation for template recognition. A new hardware platform is presented that combines a Zynq-7000 FPGA processing system and custom IP peripherals together. A frame-work for embedded system development on ARM CPU cores and FPGA fabric is introduced. The proposed hardware platform achieves up to 8 times speed-up compared to the existing FPGA based solutions.

Worldwide, traffic safety is a strong concern as traffic accidents are one of the leading causes of death. In this context, advanced driver assistance systems (ADAS) and autonomous vehicles are traffic management measures aimed at improving road safety and flow. Automatic detection and recognition of traffic signs are important for intelligent vehicles and ADAS systems. This work proposes a pipeline of digital image processing (DIP) techniques, machine learning (ML), and temporal coherence to perform the detection and recognition of Brazilian traffic signs in videos aiming an application for real-time systems to help in traffic safety and to reduce the number of fatal accidents. We are mainly interested in recognizing signs of speed limit group, no overtaking and obligatory passage, thus our detection considers the traffic sign with a circular shape and red border. For detection, the red color segmentation and the Hough transform are used to find circular regions that will be classified through the SVM algorithm in sign and not sign. For recognition of these signs, the support vector machines (SVM) are used. For speed limit signs the thresholding and contours are used to segment the digits for later classification. Our proposed method achieved an accuracy of 0.82 in detection, an increase of 18% in the number of recognized frames and 0.96 in the recognition stage using temporal coherence.

With the rapid development of the Internet of Vehicles (IoV), traffic sign detection plays an indispensable role in advancing autonomous driving and intelligent transportation. However, current road traffic sign detection technologies face challenges in terms of information privacy protection, model accuracy verification, and result sharing. To enhance system sustainability, this paper introduces blockchain technology. The decentralized, tamper-proof, and consensus-based features of blockchain ensure data privacy and security among vehicles while facilitating trustworthy validation of traffic sign detection algorithms and result sharing. Storing model training data on distributed nodes reduces the system computational resources, thereby lowering energy consumption and improving system stability, enhancing the sustainability of the model. This paper introduces an enhanced GGS-YOLO model, optimized based on YOLOv5. The model strengthens the feature extraction capability of the original network by introducing a coordinate attention mechanism and incorporates a BiFPN feature fusion network to enhance detection accuracy. Additionally, the newly designed GGS convolutional module not only improves accuracy but also makes the model more lightweight. The model achieves an enhanced detection accuracy rate of 85.6%, with a reduced parameter count of 0.34×107. In a bid to broaden its application scope, we integrate the model with blockchain technology for traffic sign detection in the IoV. This method demonstrates outstanding performance in traffic sign detection tasks within the IoV, confirming its feasibility and sustainability in practical applications.