Racing Photo Analysis Using AI Vehicle and Number Detection

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Solution Overview

Problem

Racing teams face inefficiencies in manually analyzing racing photos, leading to errors, resource diversion, and delayed insights due to the lack of automated systems for objective and consistent data processing.

Innovation Solution

A computer-implemented method and system using machine-learning models to detect and analyze attributes of racing vehicles, including vehicle identification, number recognition, orientation, and team affiliation, utilizing customized datasets and heuristic algorithms for accurate and real-time image processing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual methods are used to analyze racing photos, then teams can identify cars and their attributes, but the process is time-consuming and delays insight extraction

Engineering Contradiction:

Improveaccuracy of car identificationVSAvoidtime to analyze racing photos

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical process of examining photos with an automated machine-learning-based computer vision system. The system uses trained models to automatically detect racing vehicles, identify their numbers, recognize brands, and determine orientations from racing photos, eliminating the need for manual inspection while maintaining or improving accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service analysis where the machine-learning models autonomously process racing photos without human intervention. The models automatically extract insights including car identities, numbers, brands, and orientations, allowing teams to obtain analytical results independently without requiring manual effort.

Inventive Principle:

Principle #25Self-service

2Measurement precision

If manual analysis is performed on each photo individually, then detailed identification is possible, but the process is tedious and error-prone

Engineering Contradiction:

Improveaccuracy of attribute detectionVSAvoidthroughput of photo analysis

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces tedious manual inspection with automated machine-learning models that systematically analyze racing photos. These models detect vehicles, read numbers, recognize brands, and determine orientations automatically, eliminating human errors while processing photos at high speed without the tedium associated with manual individual examination.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system segments the photo analysis process into distinct specialized tasks handled by different machine-learning models: vehicle detection, number recognition, brand identification, and orientation determination. This segmentation allows each model to specialize in a specific attribute, improving overall accuracy and enabling parallel processing that increases throughput.

Inventive Principle:

Principle #1Segmentation

3Loss of information

If significant resources are allocated to manual photo analysis, then comprehensive data extraction is achieved, but resources are diverted from other critical areas

Engineering Contradiction:

Improvecompleteness of racing data extractionVSAvoidresource allocation for analysis

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent replaces resource-intensive manual analysis with an automated machine-learning system that extracts comprehensive racing data without requiring significant human resources. The system captures complete information including car identities, numbers, brands, and orientations, freeing up personnel and computational resources for other critical areas such as strategy development and performance optimization.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Loss of information

If manual evaluation is used to interpret racing photos, then subjective insights are obtained, but inconsistencies and biases affect accuracy

Engineering Contradiction:

Improveobjectivity of analysisVSAvoidconsistency of evaluation

Core Design Contradiction:

Loss of informationVSMeasurement precision

Solution Approach 1:

The patent replaces subjective manual evaluation with objective machine-learning models that consistently interpret racing photos without human biases. The automated system provides uniform evaluation across all photos, eliminating inconsistencies and subjective interpretations while maintaining high measurement precision through trained detection algorithms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250356674A1System and method for intelligence-based racing photo analysis

Publication Date: 2025.11.20 SIT AUTONOMOUS AG

AI summary

Systems and methods for analyzing images include an application that uses deep learning and computer vision models. Automatic analysis of photographic images allows, for example, for the identification of important elements in these images. For example, the application detects racing vehicles, vehicle numbers, vehicle details, and the orientation of these vehicles. These vehicles, typically cars, have specific attributes associated with a racing environment that can be detected with an application that comprises customized modules adapted to specific detection tasks.