Lane Line Detection Using Foreground Segmentation and Line Type Classification
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Solution Overview
Problem
Current lane line detection technologies face challenges in accurately distinguishing and processing various types of lane lines, including solid, dotted, and dotted-and-solid lines, which affects the precision of traffic regulation monitoring and vehicle safety assessments.
Innovation Solution
A method and apparatus for lane line detection that divides an image into foreground and background regions, identifies solid and dotted lines, and determines the presence of dotted-and-solid lines, improving detection accuracy by processing only relevant foreground regions and utilizing semantic segmentation and straight line fitting techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire image is processed for lane line detection, then comprehensive detection coverage is achieved, but processing time and computational resources increase
Solution Approach 1:
The image is divided into foreground region and background region, where only the foreground region containing potential lane lines is processed for detailed detection. This segmentation approach maintains detection accuracy for relevant areas while reducing overall processing time and computational resources by excluding background areas from intensive analysis.
2Adaptability or versatility
If all types of lane lines are detected using the same method, then detection consistency is maintained, but detection precision for different line types decreases
Solution Approach 1:
Different detection strategies are applied to different line types based on their characteristics. Solid lines are detected using straight line fitting methods, while dotted lines are detected by identifying sequences of discrete points with specific spacing patterns. This localized approach to each line type significantly improves detection precision while maintaining system versatility through unified foreground region processing.
Data Source
AI summary
A lane line detection method, an electronic device, and a storage medium, related to the field of artificial intelligence, and particularly related to computer vision and deep learning technologies, which can be applied to intelligent traffic scenes, are provided. The method includes: dividing an image into a foreground region and a background region; determining a solid line and a dotted line included in the foreground region; determining, according to the solid line and the dotted line comprised in the foreground region, whether a dotted-and-solid line is included in the foreground region; and determining a lane line detection result according to the solid line, the dotted line, and whether a dotted-and-solid line is comprised in the foreground region. According to the technical solution, the accuracy of lane line detection can be improved.