Road Dividing Object Detection via Region Segmentation
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Solution Overview
Problem
Conventional road dividing object detection methods are complex and time-consuming, focusing on pixel-level processing which hampers the accuracy and efficiency required for driving assistance systems.
Innovation Solution
The method detects road dividing object regions instead of individual pixels, using feature points to create a middle-layer expression, reducing the complexity of modeling and post-processing algorithms, and improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel-level processing is used for road dividing object detection, then detection detail is improved, but system complexity and processing time increase
Solution Approach 1:
The patent segments the image processing task into two levels: region-level detection using Hough transform to identify road dividing object regions, and point-level detection within those regions to locate specific feature points. This hierarchical segmentation reduces overall system complexity while maintaining detection precision.
Solution Approach 2:
The patent transitions from pure pixel-level processing to a multi-dimensional approach by first detecting road dividing object regions (spatial dimension) and then detecting feature points within those regions. This dimensional transition reduces computational complexity while preserving detection accuracy.
2Measurement precision
If pixel-level processing is used for road dividing object detection, then detection detail is improved, but processing speed decreases
Solution Approach 1:
The patent divides the detection process into two stages: first detecting road dividing object regions using Hough transform, then detecting feature points only within those identified regions. This segmentation reduces the total number of pixels requiring detailed processing, thereby improving processing speed while maintaining detection precision.
Solution Approach 2:
The patent introduces a regional dimension to the detection process, moving from direct pixel-level analysis to region-based analysis followed by point-level analysis. This dimensional approach reduces computational load and improves processing speed without sacrificing detection detail.
3Measurement precision
If complicated road dividing object modeling and post-processing are used, then detection accuracy is improved, but algorithm complexity increases
Solution Approach 1:
The patent segments the detection task into region detection (using Hough transform) and feature point detection (within regions). This segmentation eliminates the need for complicated overall modeling and post-processing while maintaining high detection accuracy through the focused point-level analysis within identified regions.
Data Source
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AI summary
Disclosed is a method of detecting at least one road dividing object. The method comprises a first obtainment step of obtaining an image including a road region; a region detection step of detecting at least one road dividing object region in the road region of the image; a feature point detection step of detecting plural feature points of the at least one road dividing object in the at least one road dividing object region; and a determination step of determining, based on the plural feature points of the at least one road dividing object in the at least one road dividing object region, the at least one road dividing object.