Dynamic Color Saturation Adjustment for Animal Collar Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image analysis techniques face low accuracy in detecting targets, such as animal collars, due to variations in lighting conditions and environmental factors, leading to incorrect detection and reduced reliability.
Innovation Solution
An analysis apparatus that adjusts detection parameters by extending the range of color saturation based on environmental conditions, such as night-time or fluorescent lighting, to improve target detection accuracy in images captured by cameras monitoring animal behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a static pattern is used to detect targets from image information, then the detection process is simple, but the accuracy of analyzing images becomes low when appearance varies due to lighting conditions
Solution Approach 1:
The patent applies dynamics by transitioning from a static detection pattern to a dynamic one that adapts to varying lighting conditions. The system dynamically adjusts the luminance threshold based on the actual image luminance characteristics, allowing the detection parameters to change according to environmental conditions while maintaining detection simplicity.
Solution Approach 2:
The patent implements parameter changes by modifying the luminance threshold parameter based on the actual image luminance distribution. Instead of using a fixed threshold, the system calculates the mean luminance of the image and sets the threshold as a proportion of this mean, allowing the detection parameters to adapt to different lighting conditions and improve accuracy.
2Measurement precision
If the luminance threshold is set too high, then dark regions are excluded from target detection, but light-colored regions may be incorrectly included; if set too low, then dark-colored target regions may be detected, but noise from dark regions increases
Solution Approach 1:
The patent applies feedback by using the actual luminance characteristics of the captured image to adjust the threshold setting. The system calculates the mean luminance of the image and uses this information to dynamically set the threshold as a proportion of the mean, creating a feedback loop that adapts to varying lighting conditions and reduces detection noise.
Solution Approach 2:
The patent implements parameter changes by adjusting the luminance threshold parameter based on the calculated mean luminance of the image. The threshold is set as a proportion (e.g., 0.5) of the mean luminance, allowing the parameter to change dynamically with lighting conditions while maintaining optimal detection accuracy and minimizing noise.
3Ease of operation
If color saturation ranges are fixed, then the detection process is straightforward, but targets cannot be correctly detected when color appearance varies due to environmental conditions
Solution Approach 1:
The patent applies dynamics by making the color saturation threshold dynamic rather than fixed. The system adjusts the saturation threshold based on the actual image characteristics and lighting conditions, allowing the detection process to adapt to environmental variations while maintaining simplicity in implementation.
Solution Approach 2:
The patent implements parameter changes by adjusting the color saturation parameter based on environmental conditions and image characteristics. The system modifies the saturation threshold to account for variations in color appearance caused by different lighting conditions, thereby maintaining reliable target detection without complicating the overall process.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method performed by a computer for image analyzation includes: receiving, by a processor of the computer, an image captured by an imaging device; specifying, by the processor of the computer, first and second areas in the image; detecting, by the processor of the computer, a target object from the image in accordance with a certain pattern indicating a range of color saturation in an analysis of a color of the target object; and extending, by the processor of the computer, the range of color saturation in processing of the detecting the target object, when luminance of the first area in the image is lower than a first threshold and luminance of the second area in the image is equal to or higher than a second threshold.