Results 11 to 20 of about 7,361 (251)
Traffic sign dataset for connected and automated vehicle operations in rural areaszenodo [PDF]
Data in BriefAccurate traffic sign recognition is essential for the safe operation of Connected and Automated Vehicles (CAVs). However, many existing datasets, such as the LISA Traffic Sign Dataset, are predominantly composed of signs found in urban environments ...
Mohammed Zakaria +2 more
doaj +2 more sources
Hierarchical Novelty Detection for Traffic Sign Recognition. [PDF]
Sensors (Basel), 2022 Recent works have made significant progress in novelty detection, i.e., the problem of detecting samples of novel classes, never seen during training, while classifying those that belong to known classes. However, the only information this task provides about novel samples is that they are unknown.
Ruiz I, Serrat J.
europepmc +5 more sources
Yolo-Based Traffic Sign Recognition Algorithm.
Comput Intell Neurosci, 2022 With the rapid development of intelligent transportation, more and more vehicles are equipped with intelligent traffic sign recognition systems, which can reduce the potential safety hazards caused by human cognitive errors. Therefore, a more safe and reliable traffic sign recognition system is the demand of drivers, and it is also the research hotspot
Li M, Zhang L, Li L, Song W.
europepmc +3 more sources
International Journal for Research in Applied Science and Engineering Technology, 2023 Abstract: Consensus on the signs and symptoms of the visitor with an understanding of water for humans is fundamentally established. But it is still difficult to identify the signs and symptoms of the guest for the laptop. Both image processing and machine learning algorithms are constantly being developed to better solve this problem.
Podila Mounika, Mudrakola Bhavani
openaire +1 more source
Traffic Sign Recognition Based on Evolutionary ResNet [PDF]
Jisuanji gongcheng, 2022 Convolutional Neural Network(CNN) has better image feature extraction performances and is widely used in traffic sign recognition.However, existing traffic sign recognition algorithms are typically based on expert experience to design an improved image ...
XIE Yirong, MA Yongjie
doaj +1 more source
Journal of Advanced Transportation, 2021 Given the impact of traffic sign combinations (TSC) on the driver’s visual recognition, this paper analyzed the influence on the driver’s visual recognition process.
Kun Liu, Hongxing Deng
doaj +1 more source
Rare Traffic Signs Recognition
Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2, 2021 Recognition of road signs is an important part of the control systems of autonomous vehicles and driver assistance systems. Modern recognition methods based on neural networks require large well-labeled datasets. Marking up data is quite expensive, but it is even more difficult to mark up rare classes of objects.
Tingir Badmaev +2 more
openaire +1 more source
Enhanced Traffic Sign Recognition with Ensemble Learning
Journal of Sensor and Actuator Networks, 2023 With the growing trend in autonomous vehicles, accurate recognition of traffic signs has become crucial. This research focuses on the use of convolutional neural networks for traffic sign classification, specifically utilizing pre-trained models of ...
Xin Roy Lim +3 more
doaj +1 more source
AUTOMATIC TRAFFIC SIGN DETECTION AND RECOGNITION USING MOBILE LIDAR DATA WITH DIGITAL IMAGES [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 This paper presents a traffic sign detection and recognition method from mobile LiDAR data and digital images for intelligent transportation-related applications. The traffic sign detection and recognition method includes two steps: traffic sign interest
H. Guan, Y. Yu, D. Li, J. Li
doaj +1 more source
Research on traffic sign recognition method based on multi-scale convolution neural network
Xibei Gongye Daxue Xuebao, 2021 In order to accurately identify the traffic sign information under different road conditions, an improved deep learning method based on Faster RCNN model is proposed.
doaj +1 more source