Why Training Data is Important for Deep Learning and Computer Vision

In 2022, deep learning algorithms will be (and already are) all the rage. These AI algorithms are the technology behind automatic inspection, object recognition, and even predictive maintenance & optimization. But if you want to know what fuels deep learning algorithms and the next generation of IoT sensors, you are at the right place. It’s training data.

Sheet Metal Box Defects

Many production facilities are now rapidly integrating AI-driven solutions for defect inspection and have been seeing significant improvements in their performance and product quality as a result.

Rail Surface Defects

Railway systems for public transport take quite a bit of maintenance to ensure the best safety measures. Moreover, there are many different kinds of rail surface defects, and some could be developing long before they’re ever noticed.

EV Battery Defects

For an electric vehicle, the battery is one of the most important components. For automakers, even a small defect in the battery can result in massive costs. As a result, it’s imperative that battery manufacturers closely inspect their products to ensure they are safe to use.

PCB Defects

With the evolution of modern computing, Printed Circuit Boards (PCBs) are critical to modern life. It’s no wonder that the PCB industry is expected to grow by nearly $13B by 2025.

Solar Panel Defects

Defective solar panels are a major problem for utility companies. To combat such anomalies, utilities are turning to UAV-based aerial inspection for defect identification.