Automatic Image Processing Parameter Tuning for Uneven Lighting
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Solution Overview
Problem
Conventional image processing systems rely on manual parameter adjustments by skilled personnel, leading to inconsistent results due to individual skill levels, and struggle with uneven lighting conditions that affect object detection accuracy.
Innovation Solution
An image processing device that automatically adjusts detection parameters by generating combinations, setting imaging conditions, judging detectability, calculating imaging ranges, and determining optimal parameters based on quantifiable criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual parameter adjustment by teaching personnel is used, then detection parameters can be adjusted, but individual skill level differences cause inconsistent adjustment results
Solution Approach 1:
The image processing device automatically evaluates and adjusts detection parameters using its own computing resources. The system generates candidate parameter combinations, evaluates their effectiveness through automated image processing and detection accuracy assessment, and selects optimal parameters without requiring external teaching personnel. This self-service mechanism eliminates variability caused by different operator skill levels.
Solution Approach 2:
The system systematically varies detection parameters (such as threshold values, processing algorithms, and detection criteria) to find optimal settings. By automatically generating multiple parameter combinations and evaluating their performance, the system identifies the best parameter set for specific imaging conditions, replacing manual trial-and-adjustment with systematic parameter optimization.
2Reliability
If lighting conditions are not optimized, then imaging can be performed under various conditions, but detection accuracy decreases due to brightness unevenness between center and edge
Solution Approach 1:
The system applies different evaluation criteria and detection parameters for different regions of the imaging range. By dividing the image into center and edge regions, the system can apply region-specific detection thresholds and parameters that account for lighting variations, improving overall detection accuracy without requiring uniform lighting conditions across the entire field of view.
Solution Approach 2:
The system dynamically adjusts detection parameters based on the actual imaging conditions detected in each image. Rather than using fixed parameters, the system adapts its detection criteria to the specific lighting and environmental conditions present, allowing reliable detection across varied imaging scenarios without requiring pre-optimized lighting setups.
3Stability of the object's composition
If automated parameter adjustment is implemented, then consistency in detection results is improved, but system complexity increases
Solution Approach 1:
The image processing device integrates multiple functions into a single automated system: it generates candidate parameter combinations, processes images, evaluates detection accuracy, and selects optimal parameters. This multi-functional integration achieves consistent detection results while consolidating complexity within the device itself rather than requiring external manual intervention.
Data Source
AI summary
The parameters of image processing are adjusted for detecting a workpiece, by quantifying the quality of the parameters of the image processing. This image processing device automatically adjusts detection parameters that are used in image processing for detecting an imaging object. The image processing device includes a detection parameter generation unit that generates detection parameter combinations, an imaging condition setting unit that sets imaging conditions for each of the detection parameter combinations, a detectability determination unit that determines whether or not the imaging object is detectable for each combination of detection parameters and imaging conditions, an imaging range calculation unit that calculates a range of imaging conditions under which the imaging object is determined, by the detectability determination unit, to have been detected, and a parameter determination unit that determines a detection parameter combination for which the calculated range of imaging conditions is the widest.