AI / ML
Audi
’ s Automated Factory Moves Closer to Industry 4.0
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“ The affordability of high compute devices has meant that heavy deep learning algorithms have now become a regular occurrence on many vision implementations . The advancement of technology from basic microcontrollers to sophisticated yet compact powerhouses has helped the manufacturing industry look at use cases like visual quality inspections , automatic part alignments and accurate packaging .
“ This is helping the industry with reduced waste , improved quality , faster inspection cycles and increased production throughput .”
Deep learning supports and boosts machine vision Deep learning-based algorithms for machine vision and predictive analysis have become a game changer for digital inspections .
These AI algorithms have wide applicability , ranging from automated site inspections to quality analysis of materials and products for detecting cracks , anomalies , orientations , colour , and thickness .
“ Deep learning has played a crucial role in enabling pre-trained models for object detection , people detection , segmentation , and classification ,” Nisal comments . “ It allows transfer learning for customising models for the industrial domain , which has resulted in faster turnaround times for machine vision .
“ More recently , there are now optimised networks for edge devices , accelerating the adoption of machine vision .”
Private clouds are expected to play an important role in the future of industrial machine vision applications , with scalability and the ability to carry out detailed data analysis being just two examples of the benefits to be expected , Michel Spruijt , Chief Revenue Officer of Brain Corp told Technology Magazine recently .
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