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Artificial Intelligence

Teaching AI to See: The Art of Logo Detection and Visual Search

2 min

How do you teach a machine to see? And more importantly, how do you help it understand what it sees? At Code for Good, we approach this challenge by combining deep learning, synthetic data, and a strong focus on transparency.

Recognizing logos in images takes more than powerful models. It requires a system that can interpret, adapt, and explain its reasoning. Our approach brings together three essential components:

Convolutional neural networks: trained to analyze visual data across tasks like classification, detection and segmentation with visual “fingerprints” as compact feature encodings that enable fast and accurate logo matching.

Synthetic data generation: that reflects real world conditions like distortion, lighting changes, and occlusion.

Interpretability tools: that let us visualize how the model’s understanding builds across its layers.

Synthetic data generation: that reflects real world conditions like distortion, lighting changes, and occlusion.

Interpretability tools: that let us visualize how the model’s understanding builds across its layers.

These elements come together to create a robust visual search system that’s built not just for performance but also for trust.

From activations to understanding

We trained a CNN to recognize logos by learning their most distinctive visual features. These features are encoded as high-dimensional “fingerprints” that enable fast and reliable matching, even across vast and noisy image datasets.

To make the process more transparent, we visualized the model’s internal activations as a series of heatmaps, arranged in a 3×3 animation. Each frame shows how the network processes a logo at different depths starting with basic edge detection in early layers and moving toward more abstract, semantic understanding in the later stages. These heatmaps shows how the model’s perception evolves, step by step.

Logo detection in the wild

To go beyond image matching and into real-world detection, we needed to train a model that could recognize logos in context on clothing, under challenging lighting, partially visible, or distorted by movement. Manual labeling for all of that would be time-consuming and inconsistent. So we built our own synthetic data pipeline.

Our training system is designed around three key steps:

Synthetic compositing: Logos are overlaid onto real clothing images in varied positions and scales to simulate real-world placements

Data augmentation: Each image is transformed through rotation, warping, color shifts and occlusion to mimic natural visual variability

Efficient training: A YOLO-based model is trained on this dataset, achieving strong performance without relying on manual annotations

Synthetic compositing: Logos are overlaid onto real clothing images in varied positions and scales to simulate real-world placements

Data augmentation: Each image is transformed through rotation, warping, color shifts and occlusion to mimic natural visual variability

Efficient training: A YOLO-based model is trained on this dataset, achieving strong performance without relying on manual annotations

The final system can detect logos quickly and reliably under a wide range of conditions — without the need for large hand-labeled datasets.

Making AI explainable

Rather than treating AI as a black box, we’ve built interpretability into the process. By examining how activations evolve from layer to layer, we get clearer insights into how and why the model arrives at certain conclusions.

This transparency isn’t just helpful during development. It’s essential for creating systems that people can trust. Whether the model is powering a brand monitoring tool or a visual search engine, the ability to explain its reasoning is part of what makes it truly usable.

Built with purpose

We believe visual intelligence should be practical, scalable, and thoughtfully designed. It’s not just about what AI can do, but how it fits into the world around us.

At Code for Good, we help businesses implement AI solutions that are good for both the planet and their bottom line. No complex consultancy plans just practical solutions that deliver real impact.