Image Anomaly Detection Using Neural Network Ensembles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional methods for anomaly detection in images, particularly synthetic aperture radar (SAR) images, are time-consuming and subjective, often relying on manual inspection, which can lead to inaccurate predictions and dangerous situations due to faulty data.
Innovation Solution
An automated anomaly detection system using a plurality of neural networks to classify probabilities of various anomalies in SAR images, allowing for parallel processing and reducing the need for manual inspection by flagging images with high anomaly probabilities for review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used for anomaly detection in images, then subjective assessment can be performed, but processing time is excessive and accuracy is reduced
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated neural network system that processes images computationally. The neural network classifies anomalies objectively without human subjectivity, achieving both higher accuracy and faster processing by substituting human visual inspection with machine-based automated detection algorithms.
Solution Approach 2:
The system enables self-service anomaly detection where the image processing system automatically identifies and classifies anomalies without requiring manual review. The neural network performs self-assessment of image quality, reducing dependency on human inspectors and eliminating processing bottlenecks associated with manual evaluation.
2Reliability
If multiple neural networks are used for anomaly classification, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent divides the anomaly detection task into multiple specialized neural networks, each trained to detect specific types of anomalies. This segmentation allows each network to focus on particular anomaly patterns, improving overall detection reliability while maintaining manageable complexity through modular architecture where each network handles a specific classification function.
Solution Approach 2:
The system employs multiple neural networks that collectively provide universal anomaly detection capabilities across different anomaly types. Each network is specialized yet contributes to a comprehensive detection system, allowing the ensemble to handle diverse anomaly patterns reliably while the modular design prevents excessive complexity through standardized processing pipelines.
3Productivity
If automated anomaly detection is implemented, then processing efficiency is improved, but subjective human assessment is lost
Solution Approach 1:
The patent replaces subjective human assessment with automated neural network evaluation that provides objective, consistent anomaly classification. The system maintains high processing efficiency while eliminating human subjectivity by using trained networks that apply uniform detection criteria across all images, ensuring reproducible results without manual variability.
Solution Approach 2:
The system incorporates feedback mechanisms where neural network predictions can be reviewed and adjusted, allowing the automated system to learn from and incorporate expert judgment. This feedback loop maintains processing efficiency while periodically refining the automated assessment through comparison with expert evaluations, bridging the gap between automated speed and human expertise.
Data Source
Figure 1
Figure 2
Figure 3A
AI summary
Methods, systems, and techniques for detecting anomalies in images are disclosed. In one aspect, an anomaly detection method for detecting anomalies in images is disclosed, comprising: receiving an image comprising at least one image frame; for each respective image frame of the at least one image frame in the image: classifying, using a plurality of neural networks, probabilities of each of a plurality of anomalies being present in the respective image frame; and generating a frame output vector for the respective image frame by combining an output vector generated from each of the plurality of neural networks for the respective image frame; and outputting the frame output vector for each of the at least one image frame.