Neural Network Bounding Box Generation for Annotation Consistency
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Solution Overview
Problem
Training neural networks for object detection is ineffective without sufficient and consistently annotated training data, leading to suboptimal performance due to inconsistencies in bounding box annotations across different datasets.
Innovation Solution
A system that uses one or more neural networks to generate modified bounding boxes, ensuring consistency across different images of the same object type by adjusting bounding boxes based on ground-truth annotations and using a label translator network to align annotation protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation of training images is performed to ensure consistent bounding boxes, then object detection accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The system uses neural networks to automatically generate and refine bounding box annotations for training images, enabling the system to self-annotate data without human intervention. The neural network processes images and generates bounding boxes that are then used to train object detection models, creating a self-sustaining annotation pipeline that eliminates manual labor while maintaining consistency.
Solution Approach 2:
A neural network acts as an intermediary between raw images and final training data, generating bounding box annotations that bridge the gap between unannotated images and consistently labeled training sets. This intermediary process automates the annotation task while ensuring uniformity across all training images.
2Quantity of substance
If diverse annotation protocols are used across different datasets to increase data variety, then training data quantity is improved, but annotation consistency deteriorates leading to suboptimal performance
Solution Approach 1:
The system standardizes annotation parameters across diverse datasets by using a unified neural network that applies consistent bounding box generation rules regardless of the source dataset. This parameter standardization allows the system to incorporate data from multiple sources while maintaining uniform annotation protocols, thus preserving both data variety and consistency.
Solution Approach 2:
The neural network annotation system serves as a universal tool that can process images from multiple different datasets and annotation protocols, unifying them under a single consistent annotation framework. This multi-functional approach enables the system to handle diverse data sources while ensuring all outputs follow the same annotation standards.
Data Source
AI summary
Apparatuses, system, and techniques use one or more neural networks to generate a modified bounding box based, at least in part, on one or more second bounding boxes.