Road Surface State Detection Using Visible and Far-Infrared Imaging
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Solution Overview
Problem
Conventional road surface state determination methods inaccurately assess the road surface condition due to the inclusion of external light sources, such as sunlight or illumination, which can interfere with the detection of reflected light, leading to incorrect determinations.
Innovation Solution
The proposed solution involves an imaging system combining a visible light camera and a far infrared camera, where the visible light camera captures images of the road surface and the far infrared camera provides temperature data, allowing for accurate determination of the road surface state by analyzing luminance and spatial frequency parameters, while excluding external light interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reflected light detection is used to determine road surface state, then the road surface state can be determined, but external light sources interfere with the detection accuracy
Solution Approach 1:
The patent segments the luminance information into two distinct components: spatial frequency parameters (derived from visible light camera images) and temperature parameters (derived from far infrared camera images). This segmentation allows the system to analyze different physical characteristics separately, with spatial frequency capturing surface texture information and temperature capturing thermal state information, thereby eliminating interference from external light sources that only affect the visible light component.
Solution Approach 2:
The patent introduces temperature information from the far infrared camera as an intermediary parameter to disambiguate road surface states. When spatial frequency alone is insufficient to determine whether low luminance indicates wet or frozen conditions, the temperature parameter serves as a mediator: temperatures below freezing indicate frozen surfaces, while temperatures above freezing indicate wet surfaces, thus resolving the ambiguity without relying solely on reflected light detection.
2Measurement precision
If spatial frequency analysis is used to differentiate road surface states, then dry and wet surfaces can be distinguished, but frozen surfaces cannot be reliably identified
Solution Approach 1:
The patent merges two distinct measurement systems: the visible light camera system that provides spatial frequency information for surface texture analysis, and the far infrared camera system that provides temperature information. By combining these complementary measurements, the system achieves comprehensive road surface state detection capability, where spatial frequency identifies dry versus wet conditions and temperature identifies frozen versus non-frozen conditions, enabling reliable detection of all four states including frozen surfaces.
3Device complexity
If only visible light camera is used for road surface detection, then the system is simpler, but detection accuracy is reduced due to external light interference
Solution Approach 1:
The patent implements a multi-functional imaging system where the visible light camera serves dual purposes: it provides spatial frequency information for surface texture analysis and simultaneously serves as a reference for the far infrared camera's temperature measurements. This multi-functionality allows the system to extract multiple types of information (luminance, spatial frequency, temperature) from coordinated imaging operations, achieving comprehensive road surface state detection without proportionally increasing system complexity.
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
This method effectively differentiates between dry, wet, and frozen road surfaces by analyzing luminance and spatial frequency parameters, providing accurate road surface state information to enhance driving safety and prevent slipping, especially in mixed conditions.
Implementation Method 1
a far infrared camera 12 that captures images of infrared light emitted by the road surface
Implementation Method 2
a visible light camera 13 that captures images of the road surface
Data Source
Figure 1
Figure 2A~3B
Figure 4
AI summary
A road surface state determination apparatus (14) of the present disclosure includes an acquisition interface (15) configured to acquire an image representing a road surface imaged by a camera, and a controller (16) configured to determine whether the road surface is wet or dry on the basis of a spatial change in luminance of pixels in a continuous region included in the image.