Edge Visual Search Probes for Fast Event Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional edge device-based event monitoring approaches are limited by time-intensive model training requirements, which hinder effectiveness in new and time-sensitive scenarios.
Innovation Solution
Implementing artificial intelligence-based automated visual data searching tools on edge devices using deep learning embedding techniques to generate lightweight probes for real-time data analysis, enabling efficient detection and reporting of relevant data without extensive training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional model training approaches are used in edge device-based event monitoring, then the system can achieve reliable detection and analysis, but the training process becomes time-intensive and ineffective for new or time-sensitive scenarios
Solution Approach 1:
The system performs preliminary action by pre-training deep learning models on diverse datasets before deployment to edge devices. This pre-training establishes a robust foundation that enables rapid adaptation to new scenarios without requiring time-intensive retraining, thus resolving the contradiction between detection reliability and training time.
Solution Approach 2:
The system changes parameters by transitioning from full model retraining to probe-based adaptation with significantly reduced training requirements. By modifying the approach from training entire models to training only probe classifiers on top of frozen feature extractors, the system maintains reliability while dramatically reducing training time for new scenarios.
2Measurement precision
If deep learning models are trained from scratch for each new scenario, then the model can be highly specialized and accurate, but the training process becomes excessively time-consuming
Solution Approach 1:
The system applies segmentation by dividing the deep learning model into two independent parts: a frozen feature extractor (pre-trained) and a trainable probe classifier. This segmentation allows the probe to be trained quickly and specifically for each new scenario while leveraging the general features already learned by the pre-trained portion, achieving both high accuracy and fast training.
Solution Approach 2:
The pre-training of the deep learning model on large-scale datasets constitutes preliminary action that provides a strong feature representation foundation. This preliminary work eliminates the need for time-consuming training from scratch for each new scenario, enabling fast and accurate probe training while maintaining high detection precision.
3Reliability
If extensive training data is collected and processed, then the model performance improves, but the data processing time and computational resources increase significantly
Solution Approach 1:
The system extracts and utilizes only the most relevant features from pre-trained models through the probe classifier approach. By taking out only the necessary components for specific scenario detection rather than processing entire datasets repeatedly, the system maintains high model performance while significantly improving data processing efficiency and reducing computational overhead.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for artificial intelligence-based techniques for automated visual data searching using edge devices are provided herein. An example computer-implemented method includes obtaining visual data from one or more edge devices; generating at least one automated searching tool by processing at least a portion of the obtained data using one or more artificial intelligence techniques; deploying the at least one automated searching tool to at least a portion of the one or more edge devices; and performing one or more automated actions based at least in part on data received, from at least a portion of the one or more edge devices, in connection with operation of the at least one automated searching tool.