Object inspection through image analysis

We nowadays make extensive use of automatic object classification based on a very limited amount of information. Sorting food items by size, weight or even color is not all that hard to automate. However, removing the items that are bruised or cracked and selecting premium food items based on surface patterns or shape is a task that is barely done in another way than manually although it is made possible with certain vision techniques.

Manual inspection for quality control is a tedious and labour intensive process. Contrary to common belief, automatic visual inspection can not only reduce labor costs, but can also increase the inspection quality.

Examples of object inspection in an industrial or agricultural setting:

• A self learning system can be trained to inspect the looks of strawberries, analyzing the color, shape, checking for irregularities and finally giving an accurate quality rating. Automatic inspection allows you to make thousands of inspections per minute, easily outperforming a human observer. (more info)
• Inspection of buildings and infrastructures for signs of cracks or wear. Combine with a robotic solution to inspect places that are dangerous or hard to reach for a human observer, like air vents or sewer systems.

Train a system yourself to new situations or products simply by presenting positive and negative examples. A self learning system can continue to learn and adept itself, whereas traditional systems would constantly require expert consultancy.