Generative Contextualized Adversarial Network for Vehicle Anomaly Detection
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Solution Overview
Problem
Existing neural network-based approaches for vehicle damage assessment require large annotated datasets of damaged vehicles, which are scarce and varied, making it difficult to detect unknown or unseen damage types and extents effectively.
Innovation Solution
A generative contextualized adversarial network (GCAN) model is used to train functional units that can detect anomalies in vehicle images by reconstructing intact-vehicle images from normal undamaged vehicle datasets, allowing for the detection of stochastic damage types and extents without requiring extensive training on damaged images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural network-based approaches use large annotated datasets of damaged vehicles for training, then detection capability for known damage types improves, but the requirement for extensive annotated training data increases and detection of unknown damage types remains insufficient
Solution Approach 1:
The patent inverts the traditional training approach by training the neural network on undamaged vehicle images instead of damaged vehicle images. The model learns what normal vehicles should look like, and then detects anomalies by identifying deviations from this learned normal state. This inversion resolves the contradiction by eliminating the need for extensive annotated damaged vehicle data while maintaining detection capability for various damage types including unknown ones.
Solution Approach 2:
The patent introduces an intermediary approach by using a generative adversarial network (GAN) as a mediator between the training data and the detection task. The GAN generates realistic undamaged vehicle images to train the model, and the anomaly detection module uses this trained model to identify damages. This intermediary structure allows the system to detect various damage types without requiring annotated damaged vehicle training data.
2Productivity
If classical CNN approaches are used for damage assessment, then repair cost estimation can be achieved, but the system requires multiple historical images and cannot effectively detect unseen damage types
Solution Approach 1:
The patent applies inversion by shifting from detecting specific damage types to detecting deviations from normality. Instead of training the model to recognize various damage patterns, it trains the model to recognize normal vehicle appearances and then identifies any deviations as anomalies. This approach enables the system to assess damages and maintain high adaptability to unseen damage types without requiring historical damaged images.
Solution Approach 2:
The patent creates a universal anomaly detection system that can handle multiple types of vehicle damages through a single trained model. By learning the general concept of normal vehicle appearance rather than specific damage types, the model becomes universally applicable to various damage scenarios including those not seen during training, thus achieving both productivity and versatility.
3Quantity of substance
If unsupervised anomaly detection systems are used, then the need for annotated damaged images is reduced, but the system complexity increases with multi-scale context-dependent deep autoencoding gaussian mixture models
Solution Approach 1:
The patent extracts and uses only the essential components needed for anomaly detection by training on undamaged images and using a relatively simple GAN architecture. It removes the complexity of multi-scale context-dependent deep autoencoding gaussian mixture models while achieving unsupervised anomaly detection with reduced annotated training data requirements. The system extracts only the necessary features from undamaged vehicles to build the detection model.
Data Source
AI summary
The present invention provides an anomaly detection method and apparatus based on a neural network which can be trained on undamaged normal vehicle images and able to detect unknown/unseen vehicle damages of stochastic types and extents from images which are taken in various contexts. The provided method and apparatus are implemented with functional units which are trained to perform the anomaly detection under a GCAN model with a training dataset containing images of undamaged vehicles, intact-vehicle frame images and augmented vehicle frame images of the vehicles.