Object Label De-Biasing for Accurate Image-Based Detection
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Solution Overview
Problem
Traditional methods for road geometry modeling and object detection in environments, such as for autonomous vehicle navigation, are resource-intensive and prone to bias in object labeling, leading to inaccurate object detection and inefficient map data reconstruction.
Innovation Solution
A method and apparatus that automatically detect objects by applying a bias transformation to corner locations in images using a processor and memory, generating de-biased corners to remove labeling bias and update map databases, enabling reliable object detection and autonomous vehicle control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used for road geometry modeling and object detection, then measurement accuracy may be maintained, but resource consumption increases and productivity decreases
Solution Approach 1:
The patent replaces manual mechanical measurement and labeling methods with automated image processing and computer vision algorithms. The system uses sensors to capture images and automatically detects objects of interest, eliminating the need for manual measurement while maintaining or improving accuracy through computational methods.
Solution Approach 2:
The system enables self-service by allowing the detection system to automatically identify and label objects without continuous human intervention. The automated processing pipeline performs object detection, labeling, and map updating autonomously, significantly improving productivity while maintaining measurement precision through algorithmic consistency.
2Reliability
If manual labeling is used for object detection training data, then labeling detail may be achieved, but bias is introduced and time consumption increases
Solution Approach 1:
The patent replaces manual labeling processes with automated image processing algorithms that detect and label objects consistently. This substitution eliminates human bias inherent in manual labeling while reducing the time required to prepare training data, thereby improving both reliability and efficiency.
Solution Approach 2:
The system creates copies of labeled data through automated processing, generating consistent training examples without requiring repeated manual labeling. The automated system can rapidly generate multiple labeled instances from the same or similar images, reducing time loss while maintaining reliability through consistent application of detection algorithms.
3Productivity
If feature detection from image data is used for object identification, then resource consumption is reduced, but detection reliability decreases
Solution Approach 1:
The patent segments the object detection process into multiple stages: image capture, feature extraction, object identification, and verification. This segmentation allows the system to process images efficiently while maintaining reliability through staged processing and validation at each step, combining the benefits of automated processing with robust error checking.
Solution Approach 2:
The system implements feedback mechanisms where detection results are validated and refined through iterative processing. The automated system uses feedback from multiple detection passes and verification steps to improve reliability while maintaining processing efficiency, ensuring that resource consumption does not compromise identification accuracy.
Data Source
AI summary
Described herein are methods of generating learning data to facilitate de-biasing the labeled location of an object of interest within an image. Methods may include: receiving sensor data, where the sensor data is a first image; determining reference corner locations of an object in the first image using image processing; generating observed corner locations of the object in the first image from the determined reference corner locations; generating a bias transformation based, at least in part, on a difference between the reference corner locations and the observed corner locations of the object in the first image; receiving sensor data from another image sensor of a second image; receiving observed corner locations of an object in the second image from a user; and applying the bias transformation to the observed corner locations of the object in the second image to generate de-biased corners for the object in the second image.