How deep learning automates inspections for packaging solutions
Increasingly , packaging products require their own custom inspection systems to perfect quality , eliminate false rejects , improve throughput , and eliminate the risk of a recall . Some of the foundational machine vision applications along a packaging line include verifying that a label on a package is present , correct , straight , and readable . Other simple packaging inspections involve presence , position , quality ( no flags , tears , or bubbles ), and readability ( barcode and date / lot codes present and scannable ) on a label .
But packaging like bottles , cans , cases , and boxes can ’ t always be accurately inspected by traditional machine vision . For applications which present variable , unpredictable defects on confusing surfaces such as those that are highly patterned or suffer from specular glare , manufacturers have typically relied on the flexibility and judgment-based decision-making of human inspectors . For applications which resist automation yet demand high quality and throughput , deep learning technology is an effective new tool at the disposal of application engineers in the packaging industry .
It can handle all different types of packaging surfaces , including paper , glass , plastics , and ceramics , as well as their labels . Be it a specific defect on a printed label or the cutting zone for a piece of packaging , Cognex Deep Learning can identify all of these regions of interest simply by learning the varying appearance of the targeted zone . Using an array of tools , Cognex Deep Learning can then locate and count complex objects or features , detect anomalies ,
44 FDPP - www . fdpp . co . uk and classify said objects or even entire scenes . And last but not least , it can recognize and verify alphanumeric characters using a pre-trained font library .
New deep learning-enabled vision systems differ from traditional machine vision because they are essentially selflearning and trained on labeled sample images without explicit programming .
Deep learning-based software uses human-like intelligence which is able to appreciate nuances like deviation and variation and outperform even the best quality inspectors at making reliably correct judgments . Most importantly , however , is that it is able to solve more complex , previously un-programmable automation challenges .
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