Machine vision is a paradigm and an umbrella term that encompasses all industrial and non-industrial applications in which a combination of hardware and software provides operational guidance to devices in operations and functioning based on the capture and processing of images. Though industrial computer vision uses many of the same algorithms and approaches as academic and military applications of computer vision, the constraints are usually very different.
Industrial vision systems are required to be more robust, more reliable, and stable compared to an academic and research-oriented vision system. They typically also cost much less than those used in military applications. Therefore, industrial machine vision systems are usually low cost, of acceptable accuracy, are highly reliable and robust, and possess high mechanical and temperature stability.
Within the past few years, Machine Vision has gained massive popularity in dynamic industries such as retail and manufacturing. These industries are leveraging the technology to enhance their customer experience, optimize the usage of resources, and achieve better quality assurance.
Although machine vision is seeing growing applications alongside the advances in technology, there are a few major applications where machine vision has proven extremely valuable.
Also Read, 7 APPLICATIONS OF MACHINE VISION
Machine vision systems include the entire system that both identifies defects and removes them from the production line. The following steps make up the machine vision process flow:
In this initial step, the system configures itself for the image-acquisition process. A machine vision system in a manufacturing plant might be inspecting various objects throughout the day. These objects may vary in size, shape, orientation, etc. The machine vision system needs to configure itself to capture the best possible image for the best results. The system might have to change its working distance or even zoom in if needed.
This entire process is automated by the software and is one of the most important steps in any machine vision process.
The first and most important step of an entire machine vision system is image acquisition. Image acquisition is the action of retrieving an image from a source, usually hardware systems like cameras, sensors, etc. It is the first and the most important step in the workflow sequence because, without an image, no actual processing is possible. In the image acquisition process, incoming light energy from an object is converted into an electrical signal by the combination of sensors that are sensitive to the particular type of energy. These systems work together in unison to provide your image-processing algorithm with the most accurate representation of the object.
The goal of the entire image acquisition process is to create an image that is usable by the machine vision technology. The imaging system’s quality is largely responsible for the success of your machine vision system. In a machine vision system, the cameras are responsible for taking the light information from a scene and converting it into digital information i.e. pixels using CMOS or CCD sensors. The sensor is the foundation of any machine vision system. Many key specifications of the system correspond to the camera’s image sensor. These key aspects include resolution, the total number of rows and columns of pixels the sensor accommodates.
Related Article: IMAGE ACQUISITION COMPONENTS
Image processing is the algorithmic process for extracting useful information from a digital image and may take place externally in a dedicated computer, or internally in a standalone vision system. Processing is performed by software and consists of several steps. First, an image is retrieved from the camera. In some cases, some minor pre-processing may be required to optimize the image and ensure that all the necessary features are highlighted. Next, the software locates the specific features, runs measurements, and compares these to the specifications agreed upon earlier. Finally, the image processing algorithm makes a decision and communicates the results. While many physical components of a machine vision system (such as lighting) offer comparable specifications, the vision system algorithms are what differentiate various systems and should top the list of key components to evaluate when comparing solutions for your requirements. Depending on the system and area of application, the software configures the parameters of the camera, makes the pass-fail decision, and communicates with the factory floor for post-process automation.
After the image processing has been carried out, the algorithm needs to communicate the pass or fail results to the mechanism responsible for acting upon the segregation process. Based on the decision of the algorithm, the system segregates the items automatically. The system then configures the vision system for the next object in the manufacturing line. All of this is part of the post-process automation.
All of the aforementioned steps are crucial for the successful functioning of a machine vision system. The failure or ineffectiveness of even one of these steps in the machine vision process flow can cause the system to fail completely.