Bounding Shape Localization Using Physical Ground Truth Tagging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for training neural networks for object detection require accurate labeling of bounding shapes, which can be time-consuming and prone to errors, especially when determining object dimensions for real-world distance estimation.
Innovation Solution
A method for automatic tagging using physical ground truth, which involves obtaining sensed information units, calculating actual dimensions of objects, determining SIU dimensions, and generating tags for accurate bounding shape localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to label bounding boxes for object detection training, then labeling accuracy can be ensured, but time consumption and labor costs increase significantly
Solution Approach 1:
The system enables automatic self-labeling of bounding boxes by utilizing the neural network model itself to generate annotations. The model processes images and automatically produces bounding box labels without human intervention, allowing the system to serve its own labeling needs while maintaining consistent accuracy standards.
Solution Approach 2:
The patent transforms the labeling task from a manual annotation process to an automated parameter extraction process. By changing the approach from human-driven visual annotation to algorithm-driven coordinate extraction, the system achieves both time efficiency and labeling precision through automated image processing and coordinate calculation.
2Productivity
If automated labeling methods are used to reduce time consumption, then productivity increases, but labeling accuracy and precision deteriorate
Solution Approach 1:
The patent replaces the mechanical manual annotation process with an automated computational system. Instead of human operators manually drawing bounding boxes, the system uses neural network inference and coordinate transformation algorithms to automatically generate precise bounding box labels, substituting human manual work with automated computational mechanics.
Solution Approach 2:
The system introduces an intermediary automated labeling module that acts as a mediator between the neural network model and the training data. This intermediary component processes model predictions and transforms them into standardized bounding box annotations, ensuring both automation and precision without direct human involvement in the labeling process.
3Manufacturing precision
If bounding boxes are manually adjusted to fit object boundaries precisely, then manufacturing precision of labels improves, but the complexity of the labeling process increases
Solution Approach 1:
The patent extracts the bounding box generation task from the complex manual annotation process. By separating the labeling function into an independent automated module that directly computes coordinates from image data and model predictions, the system achieves precise boundary fitting without the complexity of manual adjustment and verification steps.
4Reliability
If extensive manual verification is performed to ensure label quality, then reliability of training data improves, but productivity decreases
Solution Approach 1:
The system implements a feedback mechanism where the automated labeling process continuously refines its output based on model confidence scores and validation checks. High-confidence predictions are automatically accepted, while lower-confidence cases trigger automated re-processing or selective verification, creating a self-correcting system that maintains high reliability without requiring extensive manual verification of every label.
Data Source
AI summary
A method for segmentation-based generation of bounding shapes, the method may include obtaining bounding shapes that are indicative of objects, the objects were captured in a sensed information unit; generating a cropped image for each bounding shape; segmenting each cropped image to a cropped image background and a cropped image foreground; and generating, for each cropped image, an updated bounding shape that are indicative of dimensions of the cropped image foreground of the cropped image, to provide updated bounding shapes.