Six ways deep learning is used in machine vision and imaging today

Deep learning can be applied across several different application types. Here are six recent examples of deep learning technology deployed in the real-world.

Skin Care App

Deep learning concentrates on a subset of machine learning techniques, with the term "deep" generally referring to the number of hidden layers in the deep neural network. Deep learning requires substantial computing power and involves feeding large amounts of labeled data through a multi-layer neural network architecture to create a model that can classify the objects contained within the image.

While it can’t be used across all applications, the technology can in fact be applied across several different application types. Here are six recent examples of deep learning technology deployed in the real-world, in applications including the detection of diseased potatoes in the field, defect detection on PCBs, a personalized skincare recommendation phone app, and a system that enables cameras to see around corners.

Diseased Potato Detection

Pictured: Deep learning and hyperspectral imaging technologies team up for diseased potato identification

Non Line Of Sight Imaging

Pictured: Researchers combine off-the-shelf CMOS camera and illumination with convolutional neural networks for non-line-of-sight imaging.

Skin Care App

Pictured: Computer vision helps smartphone app provide personalized skin care routine.

Pcb Defects

Pictured: Deep learning software enhances PCB inspection system.

Automotive Inspection

Pictured: Deploying deep learning-based machine vision systems in automotive manufacturing applications may offer a new and useful tool that can fill gaps in manufacturing inspection.

Google Street View

Pictured: Open-source deep learning model extracts street sign locations from Google Street View.
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