Abnormality Detection Apparatus Using Discriminative and Normality Models
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Solution Overview
Problem
Existing abnormality detection techniques face challenges in handling data with a large variety of features, such as color and shape, which complicates the recognition of abnormal states due to low occurrence frequencies of abnormal events.
Innovation Solution
An abnormality detection apparatus comprising an acquisition unit, an abnormality degree computation unit, a normality degree computation unit, a determination unit, and an output unit, utilizing discriminative and normality models to compute and determine the abnormality level of input data, improving recognition accuracy by combining multiple models for feature extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single discriminative model is used for abnormality detection, then the detection process is simple, but the accuracy of handling diverse features (color, shape, etc.) is insufficient
Solution Approach 1:
The patent divides the abnormality detection task into separate discriminative models, where each model is specialized in detecting specific types of abnormalities (e.g., color-based abnormalities, shape-based abnormalities). This segmentation allows each model to focus on particular features, improving overall detection accuracy for diverse abnormality types while maintaining manageable model complexity through modular architecture.
Solution Approach 2:
The patent creates a multi-functional detection system where multiple discriminative models work together to handle various abnormality features. Each model serves a specific function (detecting particular feature types), and their combined outputs provide comprehensive abnormality detection across diverse conditions, achieving universality in handling different abnormality characteristics.
2Measurement precision
If multiple discriminative models are used to handle diverse features, then the detection accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent implements preliminary filtering and feature extraction stages before the main discriminative model analysis. By pre-processing the input data to extract relevant features and filter out irrelevant information, the system reduces the computational burden on the multiple discriminative models, thereby decreasing processing time while maintaining high detection accuracy.
Solution Approach 2:
The patent employs a hierarchical detection approach where simpler, faster models perform initial screening of obvious abnormalities, and only cases requiring more complex analysis are passed to deeper discriminative models. This partial action strategy reduces overall processing time by avoiding full complex analysis for all inputs, while still maintaining high accuracy for difficult cases.
Data Source
AI summary
An abnormality detection apparatus (100) includes an acquisition unit (110) that acquires input data, an abnormality degree computation unit (120) that has a discriminative model for computing an abnormality degree of input data, inputs the acquired input data to the discriminative model, and thereby computes an abnormality degree of the input data, a normality degree computation unit (130) that has a normality model for computing a normality degree of input data, inputs the input data to the normality model, and thereby computes a normality degree of the input data, a determination unit (140) that has a determination model for performing determination relating to an abnormality level of input data, inputs the abnormality degree and the normality degree to the determination model, and thereby performs determination relating to an abnormality level of the input data, and an output unit (150) that outputs output information based on a result of the determination.