GAN-Based Image Translation Between Heterogeneous Cameras

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Solution Overview

Problem

Existing methods fail to automatically transfer annotations from a source camera to a target camera during camera upgrades, necessitating expensive and time-consuming manual or pre-trained network-based processes.

Innovation Solution

A unified, task-agnostic system using generative adversarial networks (GANs) with shared latent representation and transformation prediction modules to translate images between distinct heterogenous cameras, enabling automatic annotation transfer without manual intervention.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual annotation process is used for target camera system, then annotation accuracy is maintained, but time consumption and cost increase significantly

Engineering Contradiction:

Improveannotation accuracyVSAvoidannotation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system creates a copy of the annotation data from the source camera and transforms it to match the target camera's image characteristics. Instead of manually annotating target camera images, the system copies annotations from source images, translates the source images to target camera style using GANs, and transfers the annotations automatically, thus maintaining accuracy while eliminating time-consuming manual annotation.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system performs preliminary translation of source images to target camera style before annotation transfer. By pre-translating the images using the trained GAN model, the system ensures that annotations are transferred to images that already match the target camera's characteristics, eliminating the need for subsequent manual adjustment and maintaining annotation accuracy.

Inventive Principle:

Principle #10Preliminary action

2Extent of automation

If pre-trained networks are used for annotation transfer, then automation is achieved, but translation accuracy and semantic preservation deteriorate

Engineering Contradiction:

Improveannotation transfer automationVSAvoidtranslation accuracy

Core Design Contradiction:

Extent of automationVSMeasurement precision

Solution Approach 1:

The system introduces an intermediary translation process using GANs between the source and target camera images. Instead of directly transferring annotations using pre-trained networks, the GAN-based translation module acts as an intermediary that transforms source images to target camera style while preserving semantics, enabling accurate automated annotation transfer without the accuracy loss associated with direct pre-trained network approaches.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system employs feedback mechanisms during the GAN training and translation process to ensure semantic preservation. The feedback loop continuously adjusts the translation to maintain semantic consistency between source and target images, allowing automated annotation transfer with high translation accuracy by correcting deviations in real-time during the translation process.

Inventive Principle:

Principle #23Feedback

3Adaptability or versatility

If camera system is migrated to new target camera, then system performance and capabilities are improved, but annotation transfer complexity increases

Engineering Contradiction:

Improvecamera system performanceVSAvoidannotation transfer complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system implements a universal GAN-based translation framework that can handle multiple camera types and configurations through a single unified approach. The shared latent representation and modular architecture allow the same translation system to work across different camera heterogeneity scenarios, reducing annotation transfer complexity despite improvements in camera system capabilities by providing a one-size-fits-all solution.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Speed

If traditional image translation methods are used, then processing speed is maintained, but semantic preservation and translation quality deteriorate

Engineering Contradiction:

Improveimage processing speedVSAvoidsemantic preservation

Core Design Contradiction:

SpeedVSMeasurement precision

Solution Approach 1:

The system replaces traditional mechanical or rule-based image translation methods with a learned GAN-based system. The GAN model automatically learns the complex mappings between source and target camera characteristics, preserving semantics more effectively than traditional methods while maintaining processing speed through efficient neural network inference and optimized translation pipelines.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250272780A1System for Translation of Images Between Two Distinct Heterogenous Cameras and a Method Thereof

Publication Date: 2025.08.28 ROBERT BOSCH GMBH

AI summary

A system for translation of images between two distinct heterogenous cameras and a method thereof is disclosed. The system includes a source camera, a target camera, two generative adversarial networks, a transformation prediction module and at least a warping module. The transformation prediction module is configured using the system and method to derive a transformation parameter. The transformation prediction module is optimized using a loss function comprising loss between a translated source image vis-à-vis a target image and a translated target image vis-à-vis a source image.