Bounding Shape Adjustment via Multi-Scale Confidence Scores
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Solution Overview
Problem
Existing computer vision technologies face challenges in accurately determining bounding shapes for objects in image data, leading to inefficient and costly labeling processes, particularly due to the difficulty in measuring the quality of labeled data and the reliance on human annotators for precise bounding box placement.
Innovation Solution
The implementation of techniques that independently determine adjustments to bounding shapes by formulating location prediction as a classification problem, using machine learning models like CNNs to extract features and provide confidence scores for edge locations, allowing for automatic refinement of bounding boxes and reducing the need for manual corrections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human annotators manually place bounding boxes for object detection, then labeling can be performed, but the process is labor-intensive, costly, and difficult to validate for quality
Solution Approach 1:
The system uses an object detection model to automatically refine bounding boxes based on detected objects in images. The model independently determines adjustments to bounding shapes without requiring manual human intervention for each bounding box, enabling the system to self-correct and improve labeling accuracy automatically.
Solution Approach 2:
The system implements a feedback mechanism where the object detection model evaluates existing bounding boxes and provides adjustments based on its detection results. Confidence scores are generated to indicate the reliability of each adjustment, creating a closed-loop system that continuously improves bounding box quality through automated feedback.
2Productivity
If automated object detection models are used to determine bounding shapes, then productivity increases, but measurement precision of bounding box quality becomes difficult to assess
Solution Approach 1:
The system generates confidence scores for each bounding box adjustment, providing a quantitative measure of quality that enables validation of automated labeling results. This feedback mechanism allows users to assess the reliability of each bounding box and make informed decisions about which adjustments to accept.
Solution Approach 2:
The patent replaces manual human validation with an automated system that uses confidence scores to indicate bounding box quality. This substitution eliminates the need for manual quality assessment while providing objective, measurable indicators of bounding box reliability through the confidence scoring mechanism.
3Measurement precision
If strict quality control is applied to bounding box labels, then accuracy is maintained, but productivity decreases due to extensive manual validation requirements
Solution Approach 1:
The system performs automated quality control through the object detection model, which independently evaluates and refines bounding boxes. This self-service approach maintains high accuracy standards while eliminating the need for time-consuming manual validation of each bounding box.
Solution Approach 2:
Confidence scores provide automated quality indicators that replace manual validation processes. The feedback mechanism allows the system to maintain strict quality control by automatically identifying and correcting poor-quality bounding boxes based on the model's confidence in its adjustments.
Data Source
AI summary
Adjustments to bounding shapes for detected objects in image data may be independently determined. A bounding shape for an object detected in image data may be obtained. Independently determined adjustments for one or more edges of the bounding shape may be determined according to a multi-scale feature map generated from different resolutions of the image data that is provided as input to different dimension decoders to determine the adjustments to the bounding shape and respective confidence scores for the adjustments. The confidence scores are evaluated with respect to a confidence threshold to determine whether to provide the adjustments to the one or more edges of the bounding shape.