Fog Detection via Grey Level Analysis
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Solution Overview
Problem
Existing fog detection systems for vehicles are unable to detect fog of light or medium density, as it does not generate a visible halo when illuminated by headlamps, limiting their effectiveness in determining fog density and visibility.
Innovation Solution
A process involving the emission of a light beam, determination of points and areas of interest in images, and analysis of grey levels to detect and quantify fog density, regardless of its density, using a device with headlamps, a control unit, and a video camera to capture and process images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a video image is used to detect high density fog at night using the luminous halo generated by headlamps, then high density fog can be detected, but light or medium density fog cannot be detected because it does not generate a sufficiently visible halo
Solution Approach 1:
The invention changes the detection parameter from visual halo intensity (which fails for light/medium density fog) to quantitative grey level analysis of pixel values. By analyzing the distribution and intensity of grey levels in the captured image, the system can detect fog across all density ranges, transforming a qualitative visual assessment into a quantitative measurement that works for all fog conditions.
2Measurement precision
If multiple methods dependent on fog density are used to detect different fog densities, then all fog densities can be detected, but the device complexity and implementation difficulty increase
Solution Approach 1:
The invention creates a universal detection method that handles all fog densities with a single approach. The grey level analysis technique serves multiple functions: it detects the presence of fog, determines fog density, and works across all visibility conditions. This single multi-functional method replaces what would otherwise require multiple specialized detection systems for different fog density ranges.
Solution Approach 2:
The invention introduces grey level analysis as an intermediary measurement technique between the headlamp illumination and the fog detection. Instead of directly observing the halo effect, the system uses grey level values of pixels as an intermediate parameter that indirectly reveals fog presence and density. This intermediary approach enables unified detection across all fog conditions without requiring multiple direct observation methods.
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
Enables the detection of fog of all densities and determination of visibility distance, allowing for optimal driving speed and safety measures, without requiring multiple cameras or cumbersome equipment, and can anticipate fog presence before entering it.
Implementation Method 1
the emission of a light beam into the vicinity of the motor vehicle by at least one of the vehicle's headlamps
Implementation Method 2
uses the luminous halo that forms an ellipse, which is generated by the reflection of headlamps from the fog
Data Source
AI summary
A process and system for detecting a phenomenon limiting the visibility for a motor vehicle. The process and system comprises the steps of determining at least one point of interest in an image captured of the environment of the vehicle (CALC_H(I)); determining a region of interest in the image (CALC_ROI(I)); determining a graph of different levels of grey on the basis of said region of interest (CALC_CL(ROI)), and determining an area in said graph of different levels of grey around said point of interest (CALC_A(CL, H)).