Sensor System Blindness Detection Using Spatial Image Planes
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Solution Overview
Problem
Conventional sensor systems struggle with sensor blindness due to disturbances, classifying the entire detection area as either completely blind or not blind, leading to the exclusion of usable sensor data from partially obscured areas.
Innovation Solution
The method converts sensor data into image data projected onto a spatially resolved image plane, allowing regional classification of sensor blindness, enabling the use of non-disturbed data while ignoring disturbed data, and utilizes neural networks for evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If sensor data are classified as completely blind or not blind, then the classification process is simple, but usable sensor data from partially obscured areas are excluded
Solution Approach 1:
The detection area is divided into multiple image areas in an image plane, allowing independent evaluation of each area. This segmentation enables partial blindness recognition where only disturbed image areas are classified as blind, while non-disturbed areas remain usable, thus preventing loss of valuable sensor data from partially obscured regions.
2Measurement precision
If sensor data are converted into image data and evaluated spatially resolved, then regional blindness recognition is achieved, but computing power requirements increase
Solution Approach 1:
The patent extracts only the necessary information from sensor data by projecting it into an image plane with focused evaluation on specific image areas. This extraction approach achieves spatially resolved blindness recognition while minimizing unnecessary computations, thereby reducing the computing power burden compared to evaluating all sensor data in full detail.
3Device complexity
If three-dimensional sensor data are compressed into two-dimensional image data, then blindness recognition is simplified, but data dimensionality is reduced
Solution Approach 1:
The patent transforms three-dimensional sensor data into a two-dimensional image plane through projection, simplifying the evaluation process. The third dimension (elevation) is mapped onto the two-dimensional plane, preserving essential spatial relationships while reducing complexity. This dimensionality change enables efficient spatially resolved blindness recognition without complete loss of spatial information.
Data Source
AI summary
A method for monitoring a sensor system. Sensor data of the sensor system are read in and information of the sensor data from different elevations of the sensor data is projected as image data into an azimuthal image plane. The information from at least two image areas of the image plane is evaluated in a spatially resolved manner in order to recognize a local sensor blindness of the sensor system. A blindness notification for an image area is output if the sensor blindness is recognized in the image area.