Optical Apparatus Fog Detection Using Grayscale Contrast and Heating
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Solution Overview
Problem
Existing optical apparatuses struggle with image clarity issues due to fog formation in humid environments, caused by temperature differences, leading to unclear images.
Innovation Solution
A method and apparatus that captures sub-images with high contrast light shield body and stripe images, calculates maximum and minimum average grayscale values, and determines fog presence using a fog function threshold, controlling a heater to adjust panel temperature for defogging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the optical apparatus is used in a humid environment with temperature difference, then fog is generated on the panel, but image clarity deteriorates
Solution Approach 1:
The system performs preliminary detection by capturing sub-images containing light shield body and stripe images, calculating grayscale values, and determining fog presence before it significantly degrades image quality. The fog discrimination method proactively identifies fog formation through temperature difference detection, allowing the heater to be activated in advance to prevent clarity deterioration.
Solution Approach 2:
The system establishes a feedback loop where the processor continuously monitors the optical apparatus panel for fog conditions by analyzing sub-images, and based on the fog discrimination result, dynamically adjusts the heater power. This closed-loop control ensures image clarity is maintained by responding to actual fog conditions rather than operating continuously or open-loop.
2Reliability
If the heater power is increased to remove fog, then image clarity is improved, but power consumption increases
Solution Approach 1:
Instead of continuously operating the heater at full power, the system applies partial heating action only when and where needed. The fog discrimination method determines specific regions with fog (through light shield stripe analysis) and activates the heater only during detected fog conditions, rather than continuous full-power operation, thus reducing unnecessary energy consumption while maintaining image clarity when required.
Solution Approach 2:
The feedback mechanism controls heater power based on actual fog detection results. When the processor determines fog is present through grayscale analysis, the heater power is increased; when no fog is detected, the heater power is reduced or turned off. This demand-based feedback control optimizes the balance between image clarity and power consumption.
3Reliability
If continuous heating is applied to prevent fog, then image clarity is maintained, but power consumption increases
Solution Approach 1:
The system replaces continuous heating with periodic fog detection and conditional heating. The processor periodically captures sub-images and performs fog discrimination analysis at intervals, activating the heater only during detected fog conditions rather than continuous operation. This periodic monitoring and conditional response maintains image clarity while significantly reducing overall power consumption compared to continuous heating.
Solution Approach 2:
The optical apparatus performs self-diagnosis through the fog discrimination method, automatically detecting fog conditions and controlling its own heater without external intervention. The system serves itself by monitoring its panel state through light shield analysis and autonomously adjusting heating, eliminating the need for continuous external control or preventive heating.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Effectively removes fog to ensure clear image capture by automatically adjusting panel temperature based on fog detection, maintaining or reducing power consumption as needed.
Implementation Method 1
The power of the heater is increased when ((the maximum average grayscale value−the minimum average grayscale value)/(the maximum average grayscale value+the minimum average grayscale value)) is less than a threshold value
Data Source
AI summary
A fog discrimination method is disclosed, including a capturing step, a calculation step, and a determining step. The capturing step includes capturing a sub-image of an image. The sub-image includes a light shield body image and a light shield stripe image. The calculation step includes calculating a maximum average grayscale value and a minimum average grayscale value of the sub-image; and calculating a fog function. The fog function is a function of the maximum average grayscale value and the minimum average grayscale value. The determining step includes determining whether the fog function is greater than or less than a threshold; and determining as being fogged when the fog function is less than the threshold.