ArticleSensors (Basel, Switzerland)2019
Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- Machine vision-based recognition of safety signs in work environments.Frontiers in public health · 2024Article
- Object Detection, Recognition, and Tracking Algorithms for ADASs-A Study on Recent Trends.Sensors (Basel, Switzerland) · 2023Review
- Traffic Sign Recognition with Deep Learning: Vegetation Occlusion Detection in Brazilian Environments.Sensors (Basel, Switzerland) · 2023Article
- Recent Advances in Traffic Sign Recognition: Approaches and Datasets.Sensors (Basel, Switzerland) · 2023Review
- Equirectangular Image Data Detection, Segmentation and Classification of Varying Sized Traffic Signs: A Comparison of Deep Learning Methods.Sensors (Basel, Switzerland) · 2023Article
- Content Swapping: A New Image Synthesis for Construction Sign Detection in Autonomous Vehicles.Sensors (Basel, Switzerland) · 2022Article
- Towards Enhancing Traffic Sign Recognition through Sliding Windows.Sensors (Basel, Switzerland) · 2022Article
- A Novel Lightweight Real-Time Traffic Sign Detection Integration Framework Based on YOLOv4.Entropy (Basel, Switzerland) · 2022Article
- Game Algorithm of Intelligent Driving Vehicle Based on Left-Turn Scene of Crossroad Traffic Flow.Computational intelligence and neuroscience · 2022Article
- A Small Network MicronNet-BF of Traffic Sign Classification.Computational intelligence and neuroscience · 2022Article
- Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm.Computational intelligence and neuroscience · 2022Article
- SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities.Sensors (Basel, Switzerland) · 2020Article
- Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network.Sensors (Basel, Switzerland) · 2020Article
- Distributed Architecture to Integrate Sensor Information: Object Recognition for Smart Cities.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.