Distributed CNN Object Detection with Self-Validating Training Data
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Solution Overview
Problem
Existing object recognition and segmentation technologies in computer vision, such as those using convolutional neural networks (CNNs), face challenges in achieving high accuracy and efficiency, particularly in recognizing and segmenting multiple types of objects in digital images, and require extensive training datasets and complex models, which can lead to false positives and negatives.
Innovation Solution
A system and method utilizing multilayer CNN models trained in multiple stages in a parallel and distributed manner, with an iteratively enhanced and self-validated training dataset, and post-model filters to improve prediction accuracy and remove false positives, specifically for object recognition and segmentation in digital images like aerial or satellite land images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional CNN models are used for object recognition and segmentation, then the system can process digital images, but it produces false positives and negatives with insufficient accuracy
Solution Approach 1:
The image is divided into multiple blocks, and the CNN model processes each block independently to generate preliminary labels. This segmentation approach allows for more granular control and validation of predictions, reducing false positives and negatives by evaluating each region separately rather than processing the entire image as a single unit.
Solution Approach 2:
The system implements a feedback mechanism where preliminary labels from the CNN model are validated against ground truth data or contextual information. Invalid predictions are corrected through iterative refinement, and the model learns from validation results to improve future predictions, thereby enhancing both accuracy and reliability.
2Productivity
If a single-stage training process is used, then the training process is simpler, but the model accuracy and training efficiency are insufficient
Solution Approach 1:
The training process is divided into multiple stages: initial training with labeled data, validation stage where predictions are checked against ground truth, and refinement stages where the model is retrained with corrected labels. This multi-stage segmentation of the training process enables both efficient processing and high accuracy by allowing iterative improvement without requiring a single complex training run.
Solution Approach 2:
The system performs preliminary training with available labeled data before validation and refinement stages. This preliminary action establishes a baseline model that can be quickly validated and corrected, improving overall training efficiency by avoiding repeated full training cycles from scratch while still achieving high accuracy through iterative refinement.
3Measurement precision
If extensive training datasets are used to improve accuracy, then the model becomes more accurate, but the device complexity and computational requirements increase
Solution Approach 1:
The system generates its own training data through the validation and correction process. By using the CNN model to generate preliminary labels, validating them against ground truth or contextual constraints, and using corrected predictions as new training samples, the system creates a self-sustaining data generation pipeline that improves accuracy without requiring external extensive datasets or complex data collection infrastructure.
Solution Approach 2:
The validation and correction processes are performed preliminarily before final model deployment. By pre-processing and correcting labels in advance, the system creates a refined training dataset that achieves high accuracy with smaller data volumes, reducing the need for extensive datasets and the associated computational complexity.
4Loss of time
If the training dataset is not validated, then the training process is faster, but the model produces more false predictions
Solution Approach 1:
The validation process is segmented into efficient stages: initial rapid validation of preliminary labels against ground truth, identification of false predictions, correction of invalid labels, and targeted retraining. This segmented validation approach minimizes time loss by focusing validation efforts on critical error-prone regions rather than validating every prediction in full detail.
Data Source
AI summary
A system for object recognition and segmentation from digital images provides an intelligent object recognition and segmentation using one or more multilayer convolutional neural network (CNN) models trained in multiple-stages and in a parallel and distributed manner to improve training speed and efficiency. The training dataset used in each of the multiple training stages for the CNN models are generated, expanded, self-validated from a preceding stage. The trained final CNN models are augmented with post-model filters to enhance prediction accuracy by removing false positive object recognition and segmentation. The system provides improved accuracy to predict object labels to append to unlabeled image blocks in digital images. In one embodiment, the system may be useful for enhancing a digital landmark registry by appending identifying labels on new infrastructure improvements recognized in aerial or satellite land images.