Simplify Deep Learning Systems with Optimized Machine Vision Lighting
Deep learning cannot compensate for or replace quality lighting. This experiment’s results would hold true over a wide variety of machine vision applications. Poor lighting configurations will result in poor feature extraction and increased defect detection confusion (false positives).
Several rigorous studies show that classification accuracy reduces with image quality distortions such as blur and noise. In general, while deep neural networks perform better than or on par with humans on quality images, a network’s performance is much lower than a human’s when using distorted images. Lighting improves input data, which greatly increases the ability of deep neural network systems to compare and classify images for machine vision applications. Smart lighting — geometry, pattern, wavelength, filters, and more — will continue to drive and produce the best results for machine vision applications with traditional or deep learning systems.