3D Object Recognition Using Modulated Light and Distance Filtering
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Solution Overview
Problem
Machine-learning-based target object recognition in vehicles faces challenges with reduced processing speed as data population increases, and accuracy issues in determining three-dimensional objects due to varying distance point specifications.
Innovation Solution
An image recognition device using temporally intensity-modulated light to generate both luminance and distance images, with a processor determining whether extracted candidates are three-dimensional objects, preventing non-3D image data from being used in the machine learning database for improved recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the data population in the machine learning database is increased to improve recognition accuracy, then the accuracy of target object recognition is improved, but the processing speed is reduced
Solution Approach 1:
The system performs preliminary filtering of image data by determining whether target object candidates are three-dimensional objects before adding them to the machine learning database. This preliminary action ensures that only valid 3D object data is accumulated, improving the quality and effectiveness of the database without requiring excessive data volume, thereby maintaining processing speed while achieving high recognition accuracy.
2Measurement precision
If multiple points are specified on the target object for measuring distances, then the measurement precision can be improved, but the accuracy of determining whether the target object is a three-dimensional object is reduced when points yield little distance difference
Solution Approach 1:
The system changes the evaluation parameter from absolute distance values to distance distribution characteristics. By analyzing the statistical distribution of distances from multiple measurement points (e.g., standard deviation, range, or histogram features), the system can reliably determine whether a target is a three-dimensional object even when individual distance differences are small, thus resolving the contradiction between measurement precision and 3D determination accuracy.
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
Enhances the reliability and accuracy of target object recognition by distinguishing between actual objects and two-dimensional images, improving processing speed and reducing erroneous recognition.
Implementation Method 1
a photoreceptor element that receives reflected light from an imaging target object irradiated with the modulated light
Implementation Method 2
a photoreceptor element that receives reflected light from an imaging target object irradiated with the modulated light
Implementation Method 3
a time-of-flight processor that calculates a time of flight of the modulated light based on the image signal output from the photoreceptor element and generates a distance image based on the calculated time of flight
Data Source
AI summary
An image recognition device includes: a luminance image generator and a distance image generator that generate a luminance image and a distance image, respectively, based on an image signal of an imaging target object output from a photoreceptor element; a target object recognition processor that extracts a target-object candidate from the luminance image using a machine learning database; and a three-dimensional object determination processor that uses the distance image to determine whether the extracted target-object candidate is a three-dimensional object. If it is determined that the target-object candidate is not a three-dimensional object, the target-object candidate extracted from the luminance image is prevented from being used, in the machine learning database, as image data for extracting a feature value of a target object.