Abstract In this paper, an improved defogging algorithm for intelligent transportation system based on image processing is proposed. According to the.

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Presentation transcript:

An Improved Intelligent Transportation Algorithm Based on Image Processing

Abstract In this paper, an improved defogging algorithm for intelligent transportation system based on image processing is proposed. According to the existing intelligent transportation system, it analyzes the principle of total variation, and introduces the principle of the total variation defogging algorithm in detail. The experimental results show that the improved total variation image defogging algorithm has lower complexity in execution, faster recognition speed and better visual defogging effect. Combining the intelligent transportation system with high-effecting image processing technology enables the transportation system to achieve better real-time performance and higher efficiency.

Introduction Literature review Methodology Results Conclusion

Introduction In traffic jam detection , license plate recognition and urban traffic monitoring system, a large number of video image processing technology has been applied. The fog and haze reduces image identification and obscures urban traffic monitoring images 4

Literature review first Calculate the ideal image gradient field of fog sky image approximately . second Construct an energy functional and solve the minimum of the energy functional. 5

Methodology Output foggy image CLAHE processing 7 CLAHE processing 6 Using Variation Method to Solve Partial Differential Equations 5 Constructing an Energy Functional 4 Using Dark Channel Theory to Obtain Image Atmospheric Intensity 3 Calculating image scene depth of field 2 Input foggy image 1

Results -Subjective comparison 7

This article algorithm Results -Objective comparison Improved algorithm time comparison chart (unit of time: s) Algorithm name Literature Algorithm This article algorithm The first group 8.4000 5.3138 The second group 10.5704 6.6234 8

Conclusion Accoring to the existing intelligent transportation system, this paper analyzes the principle of total variation, and introduces the principle of the total variation defogging algorithm in detail. Based on the total variation image defogging algorithm, an improved algorithm for traffic images under fog and hazy weather is proposed and the image has a higher definition.

THANK YOU Presenter: Zhenzi Guo Affiliation: Xi’an University of Science and Technology, P,R China Email: 16207036034@stu.xust.edu.cn