Case Study:
Transforming Brand Visibility Analysis: How Worlds View Solutions and STX Next Automated Logo Detection with AI

The Context

A global leader in brand visibility analysis partnered with Worlds View Solutions to redefine how they measure and analyze logo exposure during televised broadcasts and digital media events.

The client’s mission was to gain deeper, faster insights into brand presence and sponsorship performance, but their existing semi-manual process limited scalability and accuracy.

To overcome these challenges, Worlds View Solutions, working in collaboration with STX Next, designed and implemented a Computer Vision–powered AI system that automates logo detection and classification, transforming how visibility data is gathered, analyzed, and reported.

Challenge

The company’s existing workflow relied heavily on manual image assessment, which was time-consuming, labor-intensive, and prone to inconsistencies.

The goal was clear:

• Move from semi-manual to fully automated logo detection and classification.

• Improve accuracy and consistency in identifying brand exposure.

• Scale operations to handle large volumes of broadcast footage efficiently.

• Reduce operational costs while improving turnaround times.

In essence, the client wanted to evolve from human-dependent processes to AI-driven intelligence, unlocking new levels of precision and productivity.

The Solution

Through a focused Proof of Concept (PoC) engagement, Worlds View Solutions and STX Next co-developed a powerful AI solution capable of detecting, classifying, and analyzing brand logos across diverse media content with remarkable accuracy.

1. Automated Logo Detection

We implemented a Computer Vision model trained on 6,924 labeled images to automatically identify logos in video frames.
By combining Machine Learning algorithms with expert human validation, the model achieved an 88.19% F1-Score and 93.2% mAP@50, marking a significant improvement over previous methods.

2. Intelligent Logo Classification

Building on the detection system, we introduced a neural network–based classifier that converts identified logos into specific brand categories.
Through the use of both proprietary and public datasets, the system achieved 90.28% classification accuracy, ensuring dependable and scalable recognition across diverse content formats.

The Results

The implementation of AI-driven automation yielded immediate operational and strategic advantages, transforming how the client measures and reports on brand exposure.

• Increased efficiency:
  Automated workflows drastically reduced analysis time, accelerating insight delivery and improving turnaround on sponsorship reports.

• Enhanced accuracy:
  Advanced Computer Vision models provided precise detection and assessment, ensuring clients receive dependable exposure data.

• Cost savings:
  By minimizing manual labor, the company significantly reduced operational costs while reallocating resources toward innovation and growth.

• Scalable operations:
  The AI-driven system can process a broader range of broadcast formats and video sources, enabling the client to expand market reach and handle larger datasets effortlessly.

The Takeaway

This project showcases how strategic AI transformation can revolutionize analytical precision and scalability in brand visibility measurement.

By combining Worlds View Solutions’ innovation strategy with STX Next’s technical expertise, the client successfully transitioned to a fully automated, data-driven system, enhancing accuracy, speed, and profitability across their operations.

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