The use of machine vision in automated measurement systems is becoming increasingly popular. The reason is that more and more information needs to be extracted from the camera instead of being extracted from a dedicated sensor. The camera can be used to extract temperature information, measure dimensions, and check for the presence or absence of an object, as well as many other useful information. This makes it widely used in applications such as quality inspection, mechanical control and robot guidance, all of which have unique requirements and challenges when choosing to control hardware and software. This article outlines some of the considerations to help you choose the controller that best fits your machine vision system.
The vision system consists of one or more cameras for image acquisition and a controller for running acquisition and analysis software. These systems can have a variety of components, but the most common is a smart camera. In a smart camera, the camera and controller are integrated into one device, and the camera is connected to the desktop computer through a frame receiver or other connection ports such as Ethernet or USB. Or industrial PCs or even cameras can be directly connected to industrial control devices such as programmable logic controllers (PLCs), which manage image acquisition and slice I/O and measurement equipment.
You can use these systems to solve similar applications, but each system has different features that make it more suitable for certain usage scenarios. When choosing the controller that best fits your application, you need to consider your needs and match those requirements to the controller that best suits your mission needs. While the most obvious consideration is the controller's processing power, other factors can have a significant impact on controller selection, such as supported cameras, I/O capabilities, and the ability to integrate with existing infrastructure.
2. Processing capacityPowerful processing power can directly affect the speed at which algorithms can run and the vision system makes decisions. The processing power required for a single camera bar code detection system is clearly much lower than that of a multi-camera stereo vision system. In addition, machine vision systems such as I/O or closed-loop motion control require higher processing power to ensure that the vision components as well as the I/O and motion control components can operate stably. To reduce image processing time, some vendors now use isomorphic processing to run visual algorithms. The isomorphic approach uses a combination of CPU and GPU, FPGA, or DSP to process the image much faster than using one of the components alone. Isomorphic processing reduces the time required for image processing and even allows the image to be used as an input to a closed-loop control algorithm. Before choosing the controllers needed for the vision system, it is important to fully understand the algorithms to be used and the time it takes for the system to run these algorithms.
3. Supported camerasThe number of cameras used in image acquisition and the type of communication bus also affect the choice of controller. After you have selected the camera you want for your application, make sure the controller supports the communication bus used by the camera. Two commonly used industry standard buses in machine vision applications are USB3 Vision and GigE Visio. These standards allow the controller to connect to the camera using a standard USB 3.0 or Ethernet port, which is commonly found on consumer and industrial computers. Two standards can connect multiple cameras to a single port through a hub or switch. This is a viable way to add more cameras to your system, but keep in mind that each camera connected to the hub will share bandwidth with other cameras in the same hub. In addition, most consumer-grade switches do not support specific features such as Power over Ethernet (PoE). If you plan to use these features, or if your system does not have enough bandwidth to allow multiple cameras to share, you might want to choose a controller with multiple independent control ports so that each camera can get the full bandwidth.
4. I/O functionMost machine vision systems require some basic digital I/O to trigger the camera or read the encoder. One example is that the vision system uses a camera mounted above the conveyor belt to detect the transmitted parts. The difficulty is how to get the camera to take an image as it moves directly below the camera, unless the system can track the position of the conveyor. A common solution to this problem is to use an encoder to read the position of the conveyor belt and trigger the camera at intervals relative to the placement of the parts on the conveyor. If the part is unevenly spaced on the conveyor, a proximity sensor can be used to trigger the camera.
Some machine vision systems may require more advanced I/O capabilities. In some material analysis applications, it is often desirable to synchronize the image with the measurement data so that events in the image can be associated with the measurement data. Imagine an application that uses a camera and a strain gauge to measure the degree of bending and load of the composite when an external force is applied. These synchronized data can be used to understand the behavior of the material under external forces and even to measure the fracture point of the material. This level of synchronization requires tight integration between image acquisition and I/O, allowing measurement data to be accurately time stamped or the camera and measurement device can share the same clock. You need to understand your I/O and synchronization needs before choosing a controller. For basic I/O requirements such as triggered or asynchronous measurements, you can choose a controller with integrated or network distributed I/O. For more complex I/O requirements, you can choose a controller with integrated I/O or a connection to an I/O device via a deterministic communication protocol such as EtherCAT.
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