Traffic Sign Recognition Based on Learning Vector Quantization and Convolution Neural Network

This paper proposes a framework for developing a broad variety of soft clustering and learning vector quantization (LVQ) algorithms based on gradient descent minimization of a reformulation function. According to the proposed axiomatic approach to learning vector quantization, the development of specific algorithms reduces to the selection of a generator function. A linear generator function leads to the fuzzy c-means (FCM) and fuzzy LVQ (FLVQ) algorithms while an exponential generator function leads to entropy constrained fuzzy clustering (ECFC) and entropy constrained LVQ (ECLVQ) algorithms. The reformulation of clustering and LVQ algorithms is also extended to supervised learning models through an axiomatic approach proposed for reformulating radial basis function (RBF) neural networks. This approach results in a broad variety of admissible RBF models, while the form of the radial basis functions is determined by a generator function. This paper shows that gradient descent learning makes reformulated RBF neural networks an attractive alternative to conventional feed-forward neural networks.

Advanced driver assistance systems (ADASs) and autonomous vehicles are expected to increase safety, lower energy and fuel consumption, and lower pollutants from road traffic. The advanced driver assistance system’s major features include lane detection and tracking. Finding color line marks on the road is the technique of lane detection. The process of lane tracking aims to help the vehicle continue traveling along a predetermined course. Hence, automatic detection of lanes using convolutional neural networks (CNNs) models has gained popularity in the current economic development. This paper also aims at detecting various objects using convolutional neural networks. In this work, we offer object classification and detection, a demanding topic in computer vision and image processing. As a result, we deployed convolutional neural networks on the Keras platform with TensorFlow support. The experimental results illustrate the amount of time needed to train, test, and generate the model using a constrained computing environment. Here, we trained the system for about 80 images which have taken a couple of seconds to detect with better accuracy. Traffic sign recognition is carried out, which is a significant area of research in ADAS. It is crucial for driverless vehicles and is frequently used to read stationary or moving road signs along the side of the road. A comprehensive recognition system is made up of traffic sign detection (TSD) and categorization (TSC). The paper also aimed at traffic sign recognition, which is crucial to consider, because traffic sign recognition is typically applied to portable devices. The model’s detection accuracy is ensured as long as the speed is maintained. The model developed in this study is 99.89% accurate.

With the notion of smart cities transforming cities into digital societies and making people's lives easier in every way, Intelligent Transportation Systems have become an integral element among all. The Intelligent Transportation System (ITS) attempts to improve traffic efficiency by reducing congestion and ensuring the safety and comfort of commuters in real time. Traffic sign detection and recognition is one of the multifaceted conjunctive fields of research in ITS. In this paper, we address the issue of the TSR (traffic sign recognition) problem, i.e., classification of traffic signs along the roadside which plays a crucial role in developing advanced driver assistance and autonomous driving systems. CNN's network design has a huge impact on its performance and convergence. As a result, we use the Genetic Algorithm (GA) to automate the task of selecting a high-performance CNN (Convolutional Neural Network) Architecture for the GTSRB (German Traffic Sign Recognition Benchmark) dataset. The model is optimized through GA using multiple network configurations in the search space. Our model takes into account the limitations of the dataset, and we use certain data augmentation approaches to address the issues. We were able to attain an average accuracy of 98.2% which demonstrates the state-of-the-art performance on the publicly available dataset.KeywordsConvolutional neural networkGenetic algorithmMulticlass classificationDeep learningModel optimization

The discussion of "can machines think?" which started with the invention of the modern computer, brought along the question of "can machines design?" by researchers in the design field. These developments in information technologies have also affected the architecture. Artificial intelligence applications are encountered in many areas such as pricing estimation, energy conservation security systems of buildings, ventilation systems, user-oriented interactive design solutions, computer-aided programs used in the plan production phase and design process. When the literature on artificial intelligence applications in the architecture is reviewed, it can be seen that it generally includes shape grammars, graph theory, decision trees, constraint-based models, machine learning methods, RNN (Recursive Neural Networks), CNN (Convolutional Neural Network) and GAN (Generative Adversarial Network) algorithms. In this study, the use of artificial intelligence algorithms in architecture was examined, and an example was designed to determine the architectural structures of different periods by using CNN (Convolutional Neural Network). In the study, the open source TensorFlow library developed by Google and the Python programming language were used. Employing a statistical approach and utilizing convolutional neural networks (CNNs), a study has successfully classified the current flow patterns of buildings based on datasets comprising facades of Gothic, Modern, and Deconstructivist architectural styles. The findings demonstrate the efficacy of CNNs in accurately distinguishing the intricate details of diverse architectural styles. Recognizing elements from different periods using the CNN algorithm can examine not only individual buildings but also the relationship of buildings with their environments. It can also gain an important place in the field of conservation of the architectural discipline. The historical processes, aesthetic features and changes of protected buildings can be learned with the CNN algorithm and can guide restoration decisions. As a result of the study, the employed CNN-based model can correctly classify structures with 84.66% accuracy rate.

In this paper, we propose a traffic sign recognition algorithm based on support vector machine (SVM) and convolutional neural network (CNN) for the traffic sign recognition of intelligent transportation and unmanned vehicle. In the detection of traffic signs, by using color features, firstly we convert the RGB color space to HSV color space, which can get the regions of interest (ROI). Then extract the Histogram of Oriented Gradient (HOG) features, and determine whether it is traffic sign by support vector machine (SVM). This algorithm can successfully detect traffic signs with a high detecting rate. The following is the classification of the detected traffic signs, by using a kind of convolutional neural network, which fuse the RGB information of a picture. In the end, the `German traffic signs' dataset is used to verify the accuracy. Experimental results show that the proposed algorithm can effectively recognize traffic signs, achieving high accuracy and the algorithm complexity is lower.

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 the field of medicine, with the introduction of computer systems that can collect and analyze massive amounts of data, many non-invasive diagnostic methods are being developed for a variety of conditions. In this study, our aim is to develop a non-invasive method of classifying respiratory sounds that are recorded by an electronic stethoscope and the audio recording software that uses various machine learning algorithms.In order to store respiratory sounds on a computer, we developed a cost-effective and easy-to-use electronic stethoscope that can be used with any device. Using this device, we recorded 17,930 lung sounds from 1630 subjects.We employed two types of machine learning algorithms; mel frequency cepstral coefficient (MFCC) features in a support vector machine (SVM) and spectrogram images in the convolutional neural network (CNN). Since using MFCC features with a SVM algorithm is a generally accepted classification method for audio, we utilized its results to benchmark the CNN algorithm. We prepared four data sets for each CNN and SVM algorithm to classify respiratory audio: (1) healthy versus pathological classification; (2) rale, rhonchus, and normal sound classification; (3) singular respiratory sound type classification; and (4) audio type classification with all sound types. Accuracy results of the experiments were; (1) CNN 86%, SVM 86%, (2) CNN 76%, SVM 75%, (3) CNN 80%, SVM 80%, and (4) CNN 62%, SVM 62%, respectively.As a result, we found out that spectrogram image classification with CNN algorithm works as well as the SVM algorithm, and given the large amount of data, CNN and SVM machine learning algorithms can accurately classify and pre-diagnose respiratory audio.

The concept of smart vehicles is becoming an essential feature that ensures driver comfort and security. Smart vehicles are equipped with intelligent systems based on advanced technologies that perform a set of tasks for the mentioned purposes. Recognizing Traffic sign is one the most important systems that guarantee a high-security level. However, it is difficult to develop the best traffic sign recognition system due to numerous obstacles. such as weather conditions, geometric deformation, and most important is the material limitation. In this work, we proposed the implementation of a lightweight convolutional neural network (CNN) model on a mobile device to overcome the mentioned challenges. The proposed CNN combines high performances and low computation complexity. Evaluating the proposed model on publicly available datasets proved its efficiency. Besides, the implementation of the CNN model on the pynq platform demonstrates the possibility of using a wide range of mobile devices for the inference of the proposed model.

Annually, deep learning algorithms have proven their effectiveness in many vision-based applications, such as autonomous driving, traffic, and congestion monitoring, and so on. In computer vision, accurate traffic sign recognition and semantic road detection are vital challenges for increased safety, which are becoming a major research topic for intelligent transport systems community. In this paper, a deep learning-based driving assistance system has been proposed. To this end, we present hybrid 2D-3D CNN models based on the transfer learning paradigm to achieve better performance on benchmark real-world datasets. The primary goal of transfer learning is to improve the learning process in the target domain while transferring relevant knowledge from the source domain. We combine a pre-trained deep 2D CNN and a shallow 3D CNN to significantly reduce complexity and speed-up the training algorithm. The first model, called Hybrid-TSR, is intended to effectively address the task of traffic sign recognition. Hybrid-SRD is the second architecture that allows the semantic detection of road space through a combination of up-sampling and deconvolutional operations. The experimental results show that the proposed methods have considerable relevance in terms of efficiency and accuracy.

Facial recognition systems, which are a type of biometric systems, use the person’s facial features to identify people, and determine the information in the position between the eyes, nose, cheekbones, jawline, chin and the like and reveal a personal numerical model. The main purpose of this study is to determine the gender and age group of a selected image. By making necessary studies on the image, it is aimed to separate the gender of the person and to extract child or adult information. This study is proposed as research that performs noise reduction using the fuzzy logic fire filter algorithm and classifies the result by gender using a convolutional neural network (CNN), matrix completion and deep learning techniques. CNN algorithm, which is one of the deep facial recognition algorithms that can be used in the recognition of facial images, is the algorithm used in the study. In this study, the data obtained after the cleaning of the noises in the photographs were classified. The fuzzy logic fire filter algorithm is used to remove the noise in the image, and the image is classified according to gender and age range with the help of CNN and TensorFlow algorithms. The system has been trained using certain learning and test data sets and these algorithms. The image entered into the system is converted into a matrix expression and compared with the matrix expressions on the trained system. According to the comparison result, the image is classified as male, female, boy or girl, which matrix is closer to the value group. The data we obtained as a result of our work and the success rates show the applicability of the study. The main purpose of the study is to make gender and age group estimations according to the image with CNN classifications.

With the development of intelligent transportation systems, traffic sign recognition plays a key role in autonomous driving and driver assistance systems. The performance of traffic sign recognition algorithms directly impacts the safety of vehicles during operation. Although traditional 32-bit floating-point convolutional neural networks (CNNs) offer the advantage of high recognition accuracy, their substantial number of parameters and computational intensity make them difficult to deploy directly on resource-constrained devices like in-vehicle FPGAs. Based on this, this paper proposes a lightweight CNN model for edge computing. At the algorithmic level, a VGG-like straight-line network structure with 5 convolutional and 4 pooling layers is designed. Employing 4-bit quantization-aware training (QAT), pseudo-quantization nodes are embedded into the forward training process, enabling the network to maintain discriminative capability within the 4-bit discrete space. At the toolchain level, an end-to-end pipeline from PyTorch → QAT → HLS → bitstream is constructed. It supports one-click weight/activation bit-width pruning and RTL generation. The final generated bitstream file can be directly applied in FPGA deployment. Experiments were conducted on the GTSRB dataset. The final quantized model achieved a test accuracy of 87%, with a model size of only 0.15 MB. The hardware was deployed on a Xilinx Zynq-7020 SoC, with resource utilization falling within an acceptable range for the chip. The results indicate that the proposed method achieves an excellent balance among accuracy, resource consumption, and power usage, offering an efficient, automated, and easily deployable hardware-friendly solution for traffic sign recognition at the edge.

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 ability of autonomous driving systems to recognize traffic signs in a variety of environmental conditions is crucial to their reliability. This study uses convolutional neural networks (CNNs) to provide a novel method for improving the accuracy of traffic sign recognition systems under challenging weather situations. The research focuses on developing a CNN-based model that is trained using the augmented German Traffic Sign Recognition Benchmark (GTSRB) dataset, which contains over 50,000 labeled images of road signs across 43 categories. To attempt to overcome the limitations of traditional CNN models under adverse environmental conditions, this work presents adaptive feature extraction layers that are especially made to reduce visibility problems brought on by rain, fog, and snow. By taking a comprehensive approach, the model uses advanced data augmentation methods to simulate different weather scenarios, greatly increasing the diversity of the training dataset. Through an analysis of theoretical and practical aspects, the study demonstrates how CNNs enhance the accuracy and efficiency of road sign detection systems in a different weather condition. Research evaluates the model's effectiveness using metrics such as precision, recall, and F1-score, proving its capability to reduce false positives and accurately detect relevant road sign instances. Additionally, the paper highlights the importance of careful dataset preparation and augmentation, model optimization, and training enhancements to improve the performance of road sign detection systems. The results of this research offer benefits for intelligent transport systems, autonomous driving, and road safety, pointing towards further advancements in precise and dependable road sign recognition technology.

Traffic sign recognition is one of the urgent problems to be solved by automatic driving technology, and it is also one of the more complex problems. For the problem that the conventional convolutional neural network has not been good enough to recognize traffic signs, this paper uses an improved capsule network .The method first uses image processing to extract features of traffic signs in a complex background, remove noise, binarize traffic signs, extract the main parts, make the characteristics of traffic signs more obvious, and then input the traffic signs into the capsule network to identify. The test results on the GTSRB data set show that the improved capsule network method has an improved recognition accuracy of 2%-5% in complex scenes, which is a great improvement compared to the traditional convolutional neural network. The experimental results show that the improved capsule network method has great reference significance for the research of autonomous driving.

Considering that most convolutional neural network (CNN) models designed for traffic sign recognition (TSR) have sacrificed more resources and complicated network model development while pursuing higher performance, LeNet-5 shallow CNN with low complexity has been selected for improvement. Increasing the number of convolution kernel in the first convolution layer (C1 layer) and the third convolution layer (C3 layer) while reducing the size of the convolution kernel in C3 layer. Introducing Rectified Linear Unit (ReLU) function with better performance. The maximum pooling is introduced instead of mean pooling. Besides, the output layer employs support vector machine (SVM) to shorten the operation time. The research results demonstrate that the improved LeNet-5 network has an identification accuracy rate of 98.12% and the identification time is 0.154s for traffic signs in German Traffic Sign Recognition Benchmark (GTSRB), which could guarantee the real-time performance of the system and effectively reduce the complexity of the system on the basis of a high recognition rate.