Radar Object Detection Using Voxel-to-Pixel Image Transformation
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Solution Overview
Problem
Advanced driver-assistance systems (ADAS) using radar sensors face challenges in accurately detecting objects due to multiple reflection points generated by a single object, leading to potential misclassification of objects, especially in complex scenarios.
Innovation Solution
An electronic device employs a neural network-based object detection model that processes radar reception signals to generate a data set with voxel coordinates, transforming it into a 2D image representation, allowing for accurate object detection and classification by determining pixel coordinates and values, and updating the model based on ground truth information to improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If radar reception signals are processed to generate voxel data sets for object detection, then detection coverage and signal utilization are improved, but detection accuracy deteriorates due to multiple reflection points from a single object
Solution Approach 1:
The patent transforms 3D voxel coordinates (range, angle, velocity) into 2D image coordinates through a projection mapping process. This dimensionality reduction converts the complex 3D spatial data into a 2D image format that can be processed by conventional image-based neural networks, while preserving the essential spatial relationships needed for accurate object detection and classification
2Device complexity
If voxel coordinates are directly input into object detection models, then data processing is simplified, but detection accuracy deteriorates due to mismatch with image-based model inputs
Solution Approach 1:
The patent introduces an intermediary transformation process that converts voxel coordinates into image coordinates through a mapping function. This intermediary step acts as a bridge between the radar data representation and the image-based neural network input requirements, enabling accurate object detection by translating data formats without losing critical spatial information
3Ease of manufacture
If multiple reflection points from a single object are detected separately, then signal processing is simplified, but object classification accuracy deteriorates
Solution Approach 1:
The patent merges multiple reflection points that belong to the same object by transforming their voxel coordinates into image coordinates and then applying connected component analysis or region-based neural network processing. This merging process groups scattered reflection points into unified object regions, enabling accurate object classification while maintaining relatively simple signal processing through the use of standard image processing techniques
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 approach enhances the accuracy of object detection in ADAS systems by correctly identifying objects and reducing errors caused by multiple reflection points, thereby improving safety and reliability in various environmental conditions.
Implementation Method 1
receiving a radar reception signal reflected by an object using a radar apparatus
Implementation Method 2
the radar reception signal may include the radar transmission signal reflected by the object
Data Source
AI summary
An object detection method and apparatus are provided, the object detection method includes receiving a radar reception signal reflected by an object using a radar apparatus, generating a data set for one or more reflection points of the object by processing the radar reception signal, and detecting the object corresponding to the reflection points based on the data set using a neural network-based object detection model.