The eye is one of the most important organs of the human body and our skills greatly depend on our ability to see, recognize and distinguish objects and to estimate distances. Most jobs depend on our ability of visual perception. As amazing as the human sense of vision may be, we must acknowledge that today’s production technologies more and more often extend well beyond the limits of human visual capacities. This is where machine vision technology comes in.
Machine vision (MV) is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance in industry. The scope of MV is broad. MV is related to, though distinct from, computer vision.
Vision technology is an interdisciplinary technology that combines lighting, optics, electronics, information technology, software and automation technology. Machine vision refers the industrial application of vision technology. It describes the understanding and interpretation of technically obtained images for controlling production processes. It has evolved into one of the key technologies in industrial automation, which is used in virtually all manufacturing industries
The primary uses for machine vision are automatic inspection and industrial robot guidance. Other machine vision applications include:
- Automated Train Examiner (ATEx) Systems
- Automatic PCB inspection
- Wood quality inspection
- Final inspection of sub-assemblies
- Engine part inspection
- Label inspection on products
- Checking medical devices for defects
- Final inspection cells
- Robot guidance and checking orientation of components
- Packaging Inspection
- Medical vial inspection
- Food pack checks
- Verifying engineered components
- Wafer Dicing
- Reading of Serial Numbers
- Inspection of Saw Blades
- Inspection of Ball Grid Arrays(BGA)
- Surface Inspection
- Measuring of Spark Plugs
- Molding Flash Detection
- Inspection of Punched Sheets
- 3D Plane Reconstruction with Stereo
- Pose Verification of Resistors
- Classification of Non-Woven Fabrics
What are the advantages of using machine vision systems? First and foremost, the quality of the product is increased. Sample testing can often be replaced by 100 percent quality checks. In the example of paper production this means that every single square inch of paper produced has been reliably checked for flaws ‘on the fly’. The result is a superior product. The same applies to the printing of patterns on textiles or the production of sheet metals: The manufacturer guarantees a 100 percent perfect delivery, which is especially important if products are safety-critical. Secondly, machine vision can lead to significant cost reductions. Often, vision systems are employed in the early production stages. Defective parts are immediately removed from the manufacturing process and not finished. In many cases the removed part can be re-introduced into the production process. This saves materials. Defective parts never continue on to subsequent manufacturing stages and therefore incur no further costs. At the same time the system may become ‘self-learning’ in that it recognizes recurrent defects. This statistical information can be fed back into the process to systematically rectify the problem at the point where it originates, resulting in increased system productivity and availability. Machine vision technology is unique in its ability to resolve the trade-off between raising quality and cutting costs. Examples abound in which machine vision does both jobs at the same time.
Machine vision methods are defined as both the process of defining and creating an MV solution, and as the technical process that occurs during the operation of the solution. Here the latter is addressed. As of 2006, there was little standardization in the interfacing and configurations used in MV. This includes user interfaces, interfaces for the integration of multi-component systems and automated data interchange. Nonetheless, the first step in the MV sequence of operation is acquisition of an image, typically using cameras, lenses, and lighting that has been designed to provide the differentiation required by subsequent processing. MV software packages then employ various digital image processing techniques to extract the required information, and often make decisions (such as pass/fail) based on the extracted information.
A common output from machine vision systems is pass/fail decisions. These decisions may in turn trigger mechanisms that reject failed items or sound an alarm. Other common outputs include object position and orientation information from robot guidance systems. Additionally, output types include numerical measurement data, data read from codes and characters, displays of the process or results, stored images, alarms from automated space monitoring MV systems, and process control signals.