Vision-Based Algorithm for Precise Traffic Sign and Lane Line Matching in Multi-Lane Scenarios

With the rapid development of intelligent transportation systems, lane detection and traffic sign recognition have become critical technologies for achieving full autonomous driving. These technologies offer crucial real-time insights into road conditions, with their precision and resilience being p...

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Vydáno v:Electronics (Basel) Ročník 13; číslo 14; s. 2773
Hlavní autoři: Xia, Kerui, Hu, Jiqing, Wang, Zhongnan, Wang, Zijian, Huang, Zhuo, Liang, Zhongchao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.07.2024
Témata:
Accuracy
Algorithms
Artificial neural networks
Autonomous vehicles
Cameras
Computer vision
Coordinate transformations
Driverless cars
Edge detection
Equipment and supplies
Image filters
Image processing
Intelligent transportation systems
Lighting
Luminous intensity
Machine vision
Methods
Noise control
Object recognition
Parameter identification
Polynomials
Real time
Road conditions
Sequences
Signs
Stop lines
Target detection
Traffic control
Traffic information
Traffic intersections
Traffic signals
Traffic signs
ISSN:2079-9292, 2079-9292
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Shrnutí:With the rapid development of intelligent transportation systems, lane detection and traffic sign recognition have become critical technologies for achieving full autonomous driving. These technologies offer crucial real-time insights into road conditions, with their precision and resilience being paramount to the safety and dependability of autonomous vehicles. This paper introduces an innovative method for detecting and recognizing multi-lane lines and intersection stop lines using computer vision technology, which is integrated with traffic signs. In the image preprocessing phase, the Sobel edge detection algorithm and weighted filtering are employed to eliminate noise and interference information in the image. For multi-lane lines and intersection stop lines, detection and recognition are implemented using a multi-directional and unilateral sliding window search, as well as polynomial fitting methods, from a bird’s-eye view. This approach enables the determination of both the lateral and longitudinal positioning on the current road, as well as the sequencing of the lane number for each lane. This paper utilizes convolutional neural networks to recognize multi-lane traffic signs. The required dataset of multi-lane traffic signs is created following specific experimental parameters, and the YOLO single-stage target detection algorithm is used for training the weights. In consideration of the impact of inadequate lighting conditions, the V channel within the HSV color space is employed to assess the intensity of light, and the SSR algorithm is utilized to process images that fail to meet the threshold criteria. In the detection and recognition stage, each lane sign on the traffic signal is identified and then matched with the corresponding lane on the ground. Finally, a visual module joint experiment is conducted to verify the effectiveness of the algorithm.
Bibliografie:ObjectType-Article-1
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13142773