Query: traffic sign recognition

title: Real Time Traffic Sign Recognition Based on Efficient CNNs in the Wild
abstract: Both unmanned vehicles and driver assistance systems require solving the problem of traffic sign recognition. A lot of work has been done in this area, but no approach has been presented to perform the task with high accuracy and high speed under various conditions until now. In this paper, we have designed and implemented a detector by adopting the framework of faster R convolutional neural networks (CNN) and the structure of MobileNet. Here, color and shape information have been used to refine the localizations of small traffic signs, which are not easy to regress precisely. Finally, an efficient CNN with asymmetric kernels is used to be the classifier of traffic signs. Both the detector and the classifier have been trained on challenging public benchmarks. The results show that the proposed detector can detect all categories of traffic signs. The detector and the classifier proposed here are proved to be superior to the state of the art method. Our code and results are available online.

title: Traffic Sign Recognition Using a Multi Task Convolutional Neural Network
abstract: Although traffic sign recognition has been studied for many years, most existing works are focused on the symbol based traffic signs. This paper proposes a new data driven system to recognize all categories of traffic signs, which include both symbol based and text based signs, in video sequences captured by a camera mounted on a car. The system consists of three stages, traffic sign regions of interest (ROIs) extraction, ROIs refinement and classification, and post processing. Traffic sign ROIs from each frame are first extracted using maximally stable extremal regions on gray and normalized RGB channels. Then, they are refined and assigned to their detailed classes via the proposed multi task convolutional neural network, which is trained with a large amount of data, including synthetic traffic signs and images labeled from street views. The post processing finally combines the results in all frames to make a recognition decision. Experimental results have demonstrated the effectiveness of the proposed system.

title: A Novel Genetically Optimized Convolutional Neural Network for Traffic Sign Recognition: A New Benchmark on Belgium and Chinese Traffic Sign Datasets
abstract: Traffic signs are a key constituent of the road network and prove to be very useful for warning and guiding the drivers. In intelligent transport systems, traffic sign recognition (TSR) is indispensable for autonomous driving. However, due to the complex outdoor environment, real time recognition of traffic signs is much more challenging in comparison with many other pattern recognition tasks. Convolutional neural networks (CNNs) have an exceptional capability of recognizing patterns and are one of the most popular deep learning techniques. Finding the optimal configuration of a CNN for a task is a major challenge and is an active area of research. Genetic algorithm (GA) is a meta heuristic approach well known for its optimization power. In this paper, we propose a novel deep learning technique based on the concept of domain transfer learning for the recognition of traffic signs. This technique utilizes a newly proposed variant of the GA for finding the optimal values of the number of epochs and the learning rate parameter for each layer of the pre trained CNN model (VGG 16) To examine the effectiveness of our technique, we apply it to the following two benchmark datasets of TSR: Belgium Traffic Sign Classification (BTSC) dataset and Chinese Traffic Sign Dataset (TT100K) The results indicate that our model outperforms all the existing approaches applied to these datasets and gives a new benchmark of the recognition accuracies of 99.16% for the BTSC and 96.28% for the TT100K datasets, thus establishing the robustness of our model.

title: MR CNN: A Multi Scale Region Based Convolutional Neural Network for Small Traffic Sign Recognition
abstract: Small traffic sign recognition is a challenging problem in computer vision, and its accuracy is important to the safety of intelligent transportation systems (ITS) In this paper, we propose the multi scale region based convolutional neural network (MR CNN) At the detection stage, MR CNN uses a multi scale deconvolution operation to up sample the features of the deeper convolution layers and concatenates them to those of the shallow layer to construct the fused feature map. The fused feature map has the ability to generate fewer region proposals and achieve higher recall values. At the classification stage, we leverage the multi scale contextual regions to exploit the information surrounding a given object proposal and construct the fused feature for the fully connected layers. The fused feature map inside the region proposal network (RPN) focuses primarily on improving the image resolution and semantic information for small traffic sign detection, while outside the RPN, the fused feature enhances the feature representation by leveraging the contextual information. Finally, we evaluated MR CNN on the largest dataset, Tsinghua Tencent 100K, which is suitable for our problem and more challenging than the GTSDB and GTSRB datasets. The final experimental results indicate that the MR CNN is superior at detecting small traffic signs, and that it achieves the state of the art performance compared with other methods.

title: A Novel Traffic Sign Recognition System Combining Viola Jones Framework and Deep Learning
abstract: An automated traffic sign detection system is an important aspect of advanced driver assistance systems (ADAS) as it significantly enhances the driver's situational awareness. In this paper, a novel approach combining Viola Jones framework and deep learning, to design of a camera based traffic sign recognition system capable of detecting and identifying the traffic signs, is presented. The proposed system is designed and tested for its effectiveness under Indian road conditions. It is observed that the designed system is well capable of correctly detecting the correct traffic signs even in the presence of non standardized traffic signs.

title: Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods
abstract: This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state of the art of traffic sign classification task. First, different adaptive and non adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state of the art methods and also being more efficient in terms of memory requirements.

title: Real Time Embedded Traffic Sign Recognition Using Efficient Convolutional Neural Network
abstract: Traffic sign recognition(TSR) based on deep learning is rapidly developing. Specifically, TSR contains two technologies, namely, traffic sign classification (TSC) and traffic sign detection (TSD) However, the challenge of TSR is to ensure its efficiency, which means adequate accuracy, generalization, and speed in real time by a computationally limited platform. In this paper, we will introduce a new efficient TSC network called ENet (efficient network) and a TSD network called EmdNet (efficient network using multiscale operation and depthwise separable convolution) We used data mining and multiscale operation to improve the accuracy and generalization ability and used depthwise separable convolution (DSC) to improve the speed. The resulting ENet possesses 0.9 M parameters (1/15 the parameters of the start of the art method) while still achieving an accuracy of 98.6 on the German Traffic Sign Recognition benchmark (GTSRB) In addition, we design EmdNet' s backbone network according to the principles of ENet. The EmdNet with the SDD Framework possesses only 6.3 M parameters, which is similar to MobileNet's scale.

title: Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition
abstract: Improving driver's safety is the main goal of the advanced driver assistance system, which has been widely deployed for proactive driving security in recent years. For road driving, the advanced driver assistance system should visually recognize circular prohibition and triangular warning traffic signs to help drivers to grab complete traffic conditions. In this paper, we proposed a low computation neural assistance system for traffic sign recognition. First, we proposed shaped based detection algorithms to detect the regions, which are with circle and triangular traffic signs in designated regions of interest. For classification to those detected regions, we then suggest a convolutional neural network to achieve about 5% improvement of top 1 accuracy compared with LeNet model in German traffic sign recognition benchmarks dataset. For real applications, we also establish a Taiwanese traffic sign database to train the proposed neural network. The simulation results on self collect driving videos demonstrate that the proposed traffic sign recognition system achieved above 97% recognition rate can be effectively adopted in ADAS applications.

title: An Efficient Traffic Sign Recognition Approach Using a Novel Deep Neural Network Selection Architecture
abstract: Traffic sign classification is an important aspect of autonomous driving systems. A slight improvement on classification performance can potentially lower the rate of car accidents. In view of this, we propose three different deep convolutional neural networks in a hierarchical pattern, yet not convoluted among themselves for classifying traffic sign. A very popular and reliable traffic sign dataset called GTSRB is used to train our proposed networks. In our work, we present a novel approach to classify images. Furthermore, we modify all three convolutional neural networks over some of the existing neural nets. While modifying the networks, we redesign them based on specific requirements which may also prove handy for other datasets. Along with the new methods, we are able to reduce the computational complexity as well. On top of the new architecture, we achieve a notably higher accuracy in performance of 99.92% surpassing the state of the art performance of 99.81% In a nutshell, we trained an artificial intelligence (AI) model that learns to chose between two different AI models while classifying an image.

title: Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos
abstract: We propose a new real world attack against the computer vision based systems of autonomous vehicles (AVs) Our novel Sign Embedding attack exploits the concept of adversarial examples to modify innocuous signs and advertisements in the environment such that they are classified as the adversary's desired traffic sign with high confidence. Our attack greatly expands the scope of the threat posed to AVs since adversaries are no longer restricted to just modifying existing traffic signs as in previous work. Our attack pipeline generates adversarial samples which are robust to the environmental conditions and noisy image transformations present in the physical world. We ensure this by including a variety of possible image transformations in the optimization problem used to generate adversarial samples. We verify the robustness of the adversarial samples by printing them out and carrying out drive by tests simulating the conditions under which image capture would occur in a real world scenario. We experimented with physical attack samples for different distances, lighting conditions, and camera angles. In addition, extensive evaluations were carried out in the virtual setting for a variety of image transformations. The adversarial samples generated using our method have adversarial success rates in excess of 95% in the physical as well as virtual settings.