FEATURE Machine vision
Detecting food defects reliably with machine vision
Detecting food defects reliably with machine vision
By MVTec software engineers M
aximum speed and detection rates of preferably 100% are the requirements for quality
control in the packaging industry. The company INNDEO shows how these high requirements can be achieved with a sophisticated automation solution based on machine vision and deep learning technologies.
INNDEO, headquartered in Zaragoza, solutions for the automation of quality inspections with its INSPECTRA brand. Founded in 2016, the company primarily focused on the food industry but is now looking to support the logistics industry, too. Thus, the company has developed the Thermoseal & Label Inspector solution, which can be used to reliably inspect packaging and read labels. The device combines a wide range of sophisticated technologies like high speed and processing capture, hyperspectral vision, deep learning and
Automating packaging inspection INSPECTRA’s aim is to develop an packaging industry. The advantages machine vision are: higher detection rates of packaging defects, cost savings, and comprehensive digitalisation of production processes to monitor and improve them. In practice, many companies still carry out manual inspection processes, which introduces will reliably detect all conceivable defects in packaging, like anomalies in the sealed automation of quality control reduces costs and introduces objective criteria for sorting the products to be inspected. Although there are other machine vision solutions, not many are robust or adaptable enough. Due to lack of precision and reliability in defect
14 May 2024 | Automation
detection, users rely on manual inspection. INSPECTRA wants to eliminate exactly solutions: “In order to ensure faster inspection processes and more robust For example, quality defects in food production rate of up to two packs per second. This should enable inline rejection, which requires processing times of only a few milliseconds per image,” says Emilio at INNDEO. To achieve these goals, it was essential using machine vision. Cameras positioned at various inspection points take images of the objects, which are then processed by the integrated machine vision software MVTec HALCON, developed by the for the various applications. For example, for sealed area inspection, HALCON determines the relevant area of the image for inspection based on various parameters. For this purpose, vision technology to identify product discolouration. Hyperspectral vision technology helps with the more complex defects.
Deep learning, which is a method of
detect defects. The system learns through a training phase, identifying wrinkles
arrangement in the tray, and other quality Another application scenario is the inspection of the labelling. To detect the located, the inspection processes take place. To do this, the application uses technologies integrated in HALCON. like characters that are too close together, blurred or incomplete.
Flexibility in interface integration For the end customer, it is important that the Thermoseal & Label Inspector seamlessly integrates into the existing process environment.
“The integration of an interface was one of the biggest challenges during the implementation. This is because the various parameters of the inspection control and all the images from the various cameras have to be analysed in a very short time. It is also important not to forget that the image acquisition devices operate at enormously high speeds. So, the machine vision software has to decide in a very short time whether a package is faulty and has to be rejected,” adds de la
automationmagazine.co.uk
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102
