Label the data needed for your industrial AI with a Human in the Loop!
In industrial and manufacturing settings, precision and quality are key but it may be very hard to find available datasets for your specific use case. Our dedicated teams can generate an annotated dataset on-demand, using some of the most advanced automation tools and QC techniques.
Client success story
Read how Spanish optical sorting company Picvisa has partnered with Humans in the Loop in order to train smart sorting solutions for waste management
By using polygons, segmentation, or bounding boxes, datasets can be labeled to power computer vision models with a huge environmental impact. Labels can serve to recognise, separate, and sort recyclables, be it generic plastic, metal and paper, or complex medical or food waste.
Quality inspection is a hard model to solve with deep learning because of the scarcity of anomaly examples in the training data. We are able to use special tools for anomaly detections so as to label and extract examples with damages or deformations.
Whether you are dealing with chemicals, petrol, bodily fluids or other types of liquids, AI models can be trained to perform automatic volume estimations after a centrifuge. This requires labeling the surfaces and phase boundaries of each liquid using a polyline or a polygon.
Conveyor belt analysis
Through computer vision models, data from conveyor belts can be analysed for a variety of purposes. Through either image or video annotation with object tracking, annotated data can be used to train models for counting the number of products, classifying their type, or detecting fallen or stuck ones.
Logistics and warehouse management can be streamlined through AI in several different ways: automatic object location tracking, barcode and label detection and OCR, picking and sorting of products… and all of this can be achieved through correctly labeled data.
In construction, datasets can be labeled to track worker movement and activity on sites, to monitor progress, as well as for highlighting potentially dangerous situations. These may be simple bounding boxes but they will require going through a very large amount of data.
Wearing personal protective equipment at work, especially at construction sites or in hospitals, is crucial for safety. The monitoring process can be automated through bounding box detection and classification not only of different types of equipment but also of incorrectly worn items.
Types of annotation
Interested in having a Human in the Loop label your industrial dataset?
Get in touch with our team at Humans in the Loop and a project manager will help you find the best solution to your computer vision needs!