Vision inspection systems powered by deep learning automate manufacturing operations by detecting defects, contaminants, functional issues, and other anomalies in products. These systems enhance the manual quality processes, improving overall business quality and reducing costs. Each test record is stored and used for self-learning, increasing detection accuracy over time through continuous improvement.

Most machine vision systems come with a software tool library designed to perform various types of inspections. These systems monitor the visual appearance of materials through statistical analysis, automatically identifying potential defects on the material surface. Defects are classified based on contrast, texture, and/or geometric shape similarity, ensuring high inspection accuracy.

Vision inspection systems can perform a wide range of tasks to ensure product quality and integrity. They verify the correct placement of labels, confirm the integrity of medical product packaging by ensuring drug bottles are fully sealed and secured with tamper-evident seals, and inspect finished products for defects such as surface scratches, bent secondary diode pins, and incomplete welds. These systems also count items, such as pills in blister packs, bottles in boxes, and components in kits, and check characteristics of finished assemblies, such as the inclusion of consumer safety inserts and the completion of assembly operations. The vision inspection systems are also capable of measuring microscopic dimensions, such as the gap on spark plugs, ensuring precise and accurate measurements for quality control.