3D Object Detection Clustering for Unclassified Items
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately identifying and classifying infrequently observed objects, such as construction vehicles or portable toilets, due to their rarity in training images, leading to difficulties in navigation and safety.
Innovation Solution
A system that filters and clusters data points from 3D images like lidar or radar point clouds to identify unknown objects, generating bounding boxes around them, and uses machine learning models to improve object detection and classification, enabling better training data for autonomous vehicle systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained using conventional training images, then common objects can be identified accurately, but infrequently observed objects cannot be detected reliably
Solution Approach 1:
The system performs preliminary clustering and identification of unclassified objects in the training data before model training. By pre-processing the training images to identify and label infrequently observed objects, the system prepares enhanced training datasets that enable the machine learning model to detect rare objects reliably during actual operation.
Solution Approach 2:
The system introduces an intermediary processing stage between raw training images and model training. This intermediary stage includes clustering algorithms and unclassified object identification that bridge the gap between conventional training data and improved detection capability for rare objects.
2Measurement precision
If the system processes multiple images in real-time to identify unclassified objects, then detection accuracy improves, but processing time increases
Solution Approach 1:
The system segments the image processing task into distinct stages: initial object detection, clustering analysis of unclassified regions, and iterative refinement. By dividing the processing workflow, the system can identify unclassified objects efficiently without requiring exhaustive analysis of every pixel in real-time.
Solution Approach 2:
The system applies partial action by focusing computational resources only on unclassified regions and potential rare objects rather than processing entire images uniformly. This selective approach maintains high identification accuracy while reducing overall processing time by ignoring already-classified common objects.
Data Source
AI summary
A system receives a 3D image having multiple data points, and uses one or more filters, such as a distance filter, map filter, and/or height filter to remove certain 3D data points from the image. The system may group the data points and annotate them to identify unknown or unclassified objects within the image.