Vehicle Wheel Detection via 2D Segmentation for Pose Estimation
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Solution Overview
Problem
Current autonomous driving systems rely heavily on 2D perception methods due to the difficulty in obtaining robust ground truth data and training 3D models for accurate 3D object detection, leading to less functional and expensive solutions for vehicle control and pose estimation.
Innovation Solution
A system and method for vehicle wheel detection using image segmentation, comprising data collection and annotation, model training with deep convolutional neural networks, and real-time inference, transforming the wheel detection problem into a two-class segmentation task to leverage state-of-the-art deep learning models for accurate vehicle pose estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D perception techniques are used for accurate vehicle pose estimation, then measurement precision is improved, but device complexity and difficulty of detecting and measuring increase due to the need for robust ground truth data and properly trained 3D models
Solution Approach 1:
The patent segments the complex 3D perception task into simpler 2D wheel detection and pose estimation components. By focusing on detecting wheels in 2D images and using their geometric relationships to infer 3D vehicle pose, the system avoids the complexity of full 3D object detection while achieving accurate pose estimation.
Solution Approach 2:
The patent extracts key features (wheels) from the complex scene and uses them as sufficient indicators for vehicle pose estimation. Instead of processing entire 3D models, the system extracts wheel locations, orientations, and dimensions from 2D images to derive vehicle pose information.
2Measurement precision
If 3D perception techniques are implemented, then measurement precision is improved, but the difficulty of detecting and measuring increases due to difficulty in obtaining robust ground truth data and training 3D models
Solution Approach 1:
The patent uses 2D image copies and projections instead of requiring complex 3D ground truth data. By working with 2D image projections of wheels and applying geometric transformations, the system obtains pose information without needing difficult-to-acquire 3D annotated data for training.
Solution Approach 2:
The patent uses simple 2D image data and basic geometric models instead of expensive, complex 3D perception systems. The approach relies on readily available 2D images and simple mathematical relationships, avoiding the need for expensive sensors, complex 3D models, and difficult-to-obtain ground truth data.
3Device complexity
If 2D perception methods are used, then device complexity is reduced, but functional capability is limited for accurate vehicle control and pose estimation
Solution Approach 1:
The patent bridges 2D and 3D by using 2D wheel detections to infer 3D vehicle pose. Through geometric transformations and mathematical relationships between wheel positions in 2D images and their 3D configurations, the system recovers full 3D pose information (position, orientation, dimensions) from simple 2D inputs, enabling versatile vehicle control functionality.
Data Source
AI summary
A system and method for vehicle wheel detection is disclosed. A particular embodiment can be configured to: receive training image data from a training image data collection system; obtain ground truth data corresponding to the training image data; perform a training phase to train one or more classifiers for processing images of the training image data to detect vehicle wheel objects in the images of the training image data; receive operational image data from an image data collection system associated with an autonomous vehicle; and perform an operational phase including applying the trained one or more classifiers to extract vehicle wheel objects from the operational image data and produce vehicle wheel object data.