Groundhog Day 2022: Keep Computer Vision Accuracy Out of the Shadows

Shadows don’t just mean bad news for Punxsutawney Phil and the rest of us hoping for an early spring. They can really throw off the accuracy of computer vision models too!

Shadows pose a unique problem for model accuracy, specifically in the case of instance segmentation and object detection. Without a robust dataset that includes labeled shadows, your model may mistake the shadow as an additional detection–which can skew accuracy.

One easy way to negate this is to make sure that your training dataset has diverse images that include shadows at different angles. The more shadowed images the algorithm is exposed to the better it will be at recognizing the difference between a shadow and the object that is casting it.

Managing and retraining computer vision models for variables like shadows, light levels, and even color is important when creating computer vision models for enterprise applications. Centralized computer vision solutions like Plainsight’s vision AI platform can help solve these complex problems by enabling  users to track, manage and retrain model versions when new variables (like shadows) are found to impact model accuracy. Other common problems include: misclassification of background and foreground objects, the merging of objects, changing the shape and color of objects and missing objects.

Plainsight’s vision AI platform makes every step of the computer vision process faster and more accessible. AI-Powered tools like SmartPoly, AutoLabel and TrackForward, combined with one-click automated training, help you create richer datasets and more accurate models–beyond a shadow of a doubt.

More Plainsight Blog Posts:

5 Ways Agribusinesses Can Prevent Recalls, Shutdowns, and Delays with Vision AI

5 Ways Agribusinesses Can Prevent Recalls, Shutdowns, and Delays with Vision AI

Investments in computer vision technology can help agribusinesses and food manufacturers of all types spot signs of trouble early and stop costly, potentially deadly recalls before they happen. Deployed across the production and manufacturing cycles, these models can detect hazards ranging from contaminants and foreign objects to defective equipment and non-compliant behavior. Organizations capture hundreds of thousands of hours of visual data in the form of video footage and imagery every day, and computer vision allows these businesses to put this data to work for process transformation.

How Can Vision AI Predict and Prevent Supply Chain Disruptions?

How Can Vision AI Predict and Prevent Supply Chain Disruptions?

Introducing computer vision across the supply chain can help manufacturers predict and prevent the types of conditions that lead to these kinds of disruptions. From potential contaminants and foreign objects to defective products and packaging to unsafe or unsanitary behavior, computer vision is the key to recognizing supply chain obstacles early and stopping shortages in their tracks. AI solutions could prove especially useful in volatile, high-production periods where errors and disruptions are both especially likely and especially costly.