Radar Object Classification Using Image and Point Cloud Fusion
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Solution Overview
Problem
Conventional methods for classifying objects using radar spectra or reflections are limited in accuracy and effectiveness, as they fail to fully utilize the rich information present in radar images and point clouds, leading to potential misclassification of objects.
Innovation Solution
A computer-implemented method and device that extracts features from radar images and point clouds using neural networks, where pixels are mapped to first features, point clouds to second features, and these features are combined to determine an improved classification through a third neural network, enhancing object classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods use only radar spectra or reflections for classification, then the system complexity is low, but the classification accuracy is insufficient
Solution Approach 1:
The patent combines radar image pixels and point cloud data into a unified classification system. The radar image provides spectral information while the point cloud provides spatial and geometric information, merging these complementary data sources to achieve more accurate object classification than either method could achieve alone.
Solution Approach 2:
The patent transitions from traditional one-dimensional radar spectrum analysis to a multi-dimensional approach by incorporating point cloud data with spatial coordinates, distances, and geometric properties. This dimensional expansion enables the system to capture both spectral and geometric characteristics of objects simultaneously, significantly improving classification accuracy.
2Reliability
If only radar image pixels are used for feature extraction, then the processing is simpler, but the classification reliability is reduced due to insufficient information
Solution Approach 1:
The patent segments the object detection task into two independent parts: extracting features from radar image pixels and extracting features from point cloud data. Each segmentation can be processed by dedicated neural networks optimized for their specific data types, and the results are then combined for final classification, improving reliability while maintaining manageable processing complexity.
Solution Approach 2:
The patent creates a universal classification system that can process multiple types of radar data (pixels and point cloud) through a unified architecture. The system uses neural networks that can handle different data representations and combine them through a common classification head, making the system versatile and reliable across different object types and radar configurations.
Data Source
AI summary
A method and device for determining a classification for an object. The device is designed to provide pixels of a radar image that are assigned to the object, wherein the device is designed to provide a point cloud, wherein the point cloud comprises at least one point that represents a radar reflection assigned to the object, through at least one property assigned to the object, wherein the device is designed to extract first features that characterize the object, from the pixels, to extract second features that characterize the object, from the point cloud, and to determine the classification of the object depending on the first features and the second features.