Synthetic Logo Training Data Generation for AI Recognition Models

Solution Overview

Problem

Existing artificial-intelligence-based recognition systems face challenges in efficiently training models to recognize logos, as they require large sets of diverse images, which can be resource-intensive and time-consuming to collect, especially for new logos.

Innovation Solution

The system generates a media item training set based on a small number of logo representations by applying various transformations and combining them with other content, reducing the need for extensive data collection and enabling efficient training of recognition models.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If large sets of diverse images are collected and hand-annotated for training, then the recognition model achieves acceptable performance, but the process becomes resource intensive and time consuming

Engineering Contradiction:

Improverecognition model performanceVSAvoiddata collection time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by generating synthetic training images before actual data collection is needed. A small number of logo representations are pre-processed through various transformations (rotations, scaling, color adjustments, background compositions) to create a large training set in advance, eliminating the need for time-consuming manual image collection and annotation

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates copies of a small number of logo representations through automated transformations. Instead of collecting unique real-world images, the system generates multiple synthetic copies by applying different visual transformations to the source logo images, producing a large diverse training set from minimal input data

Inventive Principle:

Principle #26Copying

2Reliability

If large sets of diverse images are collected and hand-annotated for training, then the recognition model achieves acceptable performance, but the process becomes resource intensive

Engineering Contradiction:

Improverecognition model performanceVSAvoiddata collection resources

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system creates copies of a small number of logo representations through automated transformations. Instead of collecting unique real-world images, the system generates multiple synthetic copies by applying different visual transformations to the source logo images, producing a large diverse training set from minimal input data

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system replaces the mechanical process of manual image collection, storage, and annotation with an automated computational system. Software-based image generation and transformation algorithms substitute for physical data gathering operations, eliminating the need for extensive human labor and complex data management infrastructure

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Quantity of substance

If computer-assisted searches are used to collect large sets of images, then some data can be obtained, but the process remains resource intensive and time consuming

Engineering Contradiction:

Improvetraining data quantityVSAvoiddata collection time

Core Design Contradiction:

Quantity of substanceVSLoss of time

Solution Approach 1:

The system performs preliminary actions by generating synthetic training images before actual data collection is needed. A small number of logo representations are pre-processed through various transformations (rotations, scaling, color adjustments, background compositions) to create a large training set in advance, eliminating the need for time-consuming manual image collection and annotation

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12205361B2System and method for facilitating graphic-recognition training of a recognition model

Publication Date: 2025.01.21 NEBIUS BV

AI summary

Methods and computer readable media for facilitating training of a recognition model. An embodiment includes generating media items based on information associated with a representation of a graphic, the information including content other than the graphic, content based on at least one transformation parameter set, and content comprising the graphic integrated with the other content, then using a recognition model to process the media items to generate predictions related to recognition of the graphic for the media items, the generated predictions including an indication of a predicted location of the graphic in a first media item. The process also includes presenting an indication of the predicted location on an area of the first media item via a user interface to a user, then obtaining a reference feedback set that includes reference indications related to recognition of the graphic for the media items and including user feedback concerning the indication of the predicted location of the graphic, and then updating the recognition model based on the reference feedback.