Image Processing Device Partition Line Detection
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Solution Overview
Problem
Conventional image processing devices for autonomous vehicles face issues with erroneous detection of parking frame partition lines, particularly in regions with similar patterns like gratings, leading to increased processing load and errors.
Innovation Solution
An image processing device equipped with a detection unit and a judging unit that identifies and excludes improper regions based on edge patterns and feature patterns, preventing erroneous detection of partition lines and reducing processing load by recognizing grating regions as improper for detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the detection unit processes all regions in image data to detect partition lines, then detection coverage is improved, but processing load increases and erroneous detection occurs in improper regions
Solution Approach 1:
The image data is divided into multiple regions, and the judging unit evaluates each region to determine whether it is an improper region. This segmentation allows the system to process only relevant regions, reducing overall processing load while maintaining detection accuracy in proper regions.
Solution Approach 2:
The judging unit performs preliminary evaluation of regions before the detection unit processes them. By identifying and excluding improper regions in advance based on edge pattern analysis, the system prevents wasted processing on regions that cannot contain valid partition lines, thereby reducing processing load without compromising detection accuracy.
2Measurement precision
If the detection unit processes all regions in image data, then detection coverage is improved, but erroneous detection of partition lines increases in improper regions
Solution Approach 1:
The judging unit performs preliminary evaluation of regions before detection, identifying improper regions that would lead to erroneous detection. By excluding these regions in advance, the system prevents detection errors while maintaining comprehensive coverage of all proper regions.
Solution Approach 2:
The system converts the harmful effect of processing improper regions (which causes erroneous detection) into a benefit by using the same edge pattern analysis to identify and exclude these regions. The edge pattern evaluation that would otherwise lead to errors is repurposed to prevent errors by marking regions for exclusion.
3Productivity
If the judging unit excludes improper regions from detection, then processing load is reduced, but detection coverage may be limited
Solution Approach 1:
The system applies different processing strategies to different regions: improper regions are excluded from detection, while proper regions undergo full detection processing. This local differentiation ensures that processing efficiency is improved without compromising detection coverage in regions where partition lines actually exist.
Solution Approach 2:
The image data is segmented into proper and improper regions, allowing the system to apply detection processing only where needed. This segmentation maintains comprehensive detection coverage in proper regions while eliminating wasted processing in improper regions, thus improving efficiency without limiting actual detection coverage.
Data Source
AI summary
An image processing device includes: a detection unit which detects a target object based on edge information acquired from image data; and a judging unit which judges whether a certain region in the image data is an improper region that is not suitable for detection of the target object based on an edge pattern of edge information in the certain region and a feature pattern indicating a feature of the target object, and the detection unit detects the target object excluding a region that has been judged to be the improper region by the judging unit.