A European robotics company building intelligent automation systems for the laundry industry partnered with Intellisane AI to solve one of their key computer vision challenges: identifying and understanding fine-grained parts of garments through annotated image data.
Their goal? To develop a robotic system capable of recognizing, handling, and sorting various types of clothing — from t-shirts to towels — in real-world, unstructured environments.
The client’s robotics platform needed computer vision models trained to detect intricate garment parts such as:
The objective was to train deep learning models to distinguish these sections with high precision, enabling robots to execute tasks like folding, picking, and sorting garments accurately. This required semantic segmentation, instance segmentation, and bounding box annotations across thousands of challenging real-world images.
This was not a typical annotation task. The project presented real-world data challenges that demanded more than technical labeling skills:
1. Overlapping and Occluded Garments
Garment parts were often hidden under folds or stacked over each other, making boundaries hard to define — especially between similar parts like hems and narrow seams.
2. Motion Blur and Wrinkles
Some images captured garments mid-motion or with natural wrinkles, distorting their appearance and making annotations difficult.
3. Visually Similar Textures
Different garment parts sometimes looked nearly identical due to texture, lighting, or color — e.g., Terry vs. Shoulder.
4. Ambiguous Garment Types
With some items looking too similar (like vests and sleeveless tees), classification required human-level garment knowledge.
At Intellisane AI, we believe domain expertise is just as important as annotation tools.
Instead of relying solely on visual guesswork, we trained our annotators practically. For items like t-shirts, towels, or uniforms, we purchased physical products from the market and trained our team by physically showing the structure of each part. This tactile, human-in-the-loop approach helped our team:
This hands-on training led to significantly improved consistency, accuracy, and labeling speed, even in the most complex batches.
Thanks to our structured process, domain-specific training, and rigorous QA workflows, we delivered exceptional results:
The client reported measurable performance gains in detection accuracy and system efficiency, crediting our team for enabling production-ready AI in a challenging, unstructured domain.
“Through this collaboration, the client successfully built a more intelligent and accurate robotic system for handling garments — powered by clean, high-quality image annotations. By addressing edge cases, training challenges, and real-world variability, Intellisane AI enabled faster model training, improved detection accuracy, and quicker product deployment
Finally
This project proves that real-world AI needs real-world understanding. At Intellisane AI, our blend of deep annotation expertise, domain-driven training, and commitment to long-term success sets us apart as a trusted partner for robotics, automation, and computer vision companies worldwide.
Key Results:
Intellisane AI boosted robotic laundry systems with accurate cloth part labeling using semantic and instance segmentation.
