Two-Stage Neural Network for Radar Object Detection
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Solution Overview
Problem
Current methods for detecting objects using radar point clouds face high computational effort and inefficiency, especially when extending to multiple object classes, due to redundant operations and increased complexity.
Innovation Solution
A two-stage neural network architecture is employed for object detection, where the first stage performs semantic segmentation to rough-approximate spatial conditions of geometrical shapes, and the second stage refines these approximations with confidence scoring, allowing for efficient detection of multiple object classes without significantly increasing computational demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a basic neural network architecture with max-pooling is used for object detection, then object classification and bounding box estimation can be performed, but computational effort increases significantly due to patch proposal and anchor boxes
Solution Approach 1:
The patent applies segmentation by dividing the object detection task into two distinct stages: a first stage that performs semantic segmentation to classify data points and identify object regions, and a second stage that refines bounding box estimates only for the segmented object regions. This segmentation eliminates the need for exhaustive patch proposals and anchor boxes across the entire point cloud, thereby reducing computational effort while maintaining detection accuracy.
Solution Approach 2:
The first stage of the neural network performs preliminary semantic segmentation to identify and classify data points belonging to objects before the second stage refines bounding box estimates. This preliminary classification action filters out background points and focuses subsequent computational resources only on relevant object regions, avoiding redundant operations and reducing overall computational effort.
2Adaptability or versatility
If the object detection approach is extended to multiple object classes, then detection capability increases, but computational effort increases further since copies of the whole network would be required
Solution Approach 1:
The patent implements multi-functionality by designing a single universal first-stage neural network that performs semantic segmentation for multiple object classes simultaneously, rather than requiring separate network copies for each class. The second stage then refines bounding boxes for detected objects across all classes. This universal approach enables multi-class detection while avoiding the computational overhead of maintaining multiple separate network instances.
Solution Approach 2:
The two-stage architecture segments the detection process such that the first stage handles class classification for all objects, and the second stage handles bounding box refinement. This segmentation allows the system to support multiple object classes efficiently by performing classification once for all classes in the first stage, rather than requiring separate network passes for each class.
3Reliability
If redundant operations are performed during classification, then comprehensive object analysis is achieved, but inconsistent determination of bounding boxes occurs
Solution Approach 1:
The patent resolves the inconsistency issue by segmenting the detection pipeline into two stages with distinct responsibilities: the first stage performs comprehensive semantic segmentation and classification, while the second stage performs bounding box refinement exclusively on the classified object points. This segmentation ensures that each stage performs its specialized function without redundant operations that could lead to inconsistent bounding box determinations.
Solution Approach 2:
The first stage performs preliminary classification and identification of object points before the second stage refines bounding boxes. This preliminary action ensures that only confidently classified object points are passed to the bounding box refinement stage, eliminating redundant operations on background or misclassified points that would cause inconsistency, while maintaining comprehensive object analysis.
Data Source
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AI summary
A method is provided for detecting objects by using a radar sensor and by using a device configured to establish a neural network. A plurality of raw radar data points is captured. At least one object class is defined comprising a predefined object type and a geometrical shape for enclosing an object. Via a first stage of the neural network, a semantic segmentation is performed for the data points regarding the object class and background, and for each data point, a rough approximation is estimated for a spatial condition of the geometrical shape. Based on this rough approximation and via a second stage of the neural network, a subset of the data points is selected based on the semantic segmentation, and for each data point of the subset, a refined approximation for the spatial condition of the geometrical shape and a confidence score for the refined approximation are estimated.