In modern production facilities, users are more frequently combining two different strands of camera technology. Classic machine vision cameras manage inspection tasks and yield management, while network cameras (also called IP cameras) handle process monitoring and bringing production to a standstill when necessary.

Small and light cameras like the Basler ace are especially well suited for use in robotics applications.

In many ways, the two camera breeds are actually similar. Industrial GigE cameras typically work with the same Gigabit Ethernet technology as IP cameras. Both camera types also use the same technical protocols, including TCP/IP and UDP. The similarities, in fact, make the separate technologies — both engineered from the ground up to serve different purposes — easy to combine. Customers now want to implement both camera types to fit their specific applications, and new products are being developed that combine the best features from the IP and machine vision devices.

IP vs. Classic Industrial Cameras

The core differences between a classic machine vision camera and a network (or IP) device come in the areas of image data compression, multi-streaming, and real-time compatibility.

IP cameras are specially designed to work with low bandwidths to ensure that they can fit seamlessly in existing networks without overloading them. The camera is configured via a PC on the network to send a compressed video stream, such as MPEG-4, MJPEG, or H.264. The IP camera primarily provides a strong visual impression on the operator’s monitor.

Industrial cameras, by contrast, are engineered to work with large bandwidths and optimal image quality as part of closed image processing systems. While network cameras compress the image data down to a fraction of its original data volume, in an effort to reduce the bandwidth required to transport that data, industrial cameras deliver raw images. The unprocessed images allow users to review even the most minute of details, as is necessary for quality inspections or detail measurements. If image data is missing and the error occurs precisely in that area, leading to a false positive by the inspection system, then the system has failed in its mission.

IP cameras are also capable of multi-streaming, meaning the camera sends multiple streams in different compression formats, such as H.264, MPEG-4, and MJPEG. Each end device then accesses a suitable stream. An operator, for example, can call up a high-resolution MJPEG stream offering strong detailing on his or her monitor, even while a space-saving version in the H.264 format is submitted for archiving. Machine vision cameras work with post-processed data that has already been evaluated, such as production statistics. The devices also optimize equipment performance and adapt the equipment to eliminate recurrent errors. In general, the images are only stored after inspection.

Industrial camera and IP camera systems are typically found within the same production environment, carrying out their own specific tasks.

Real-time capability, another functional distinction between the two technologies, means that image capture starts immediately after a trigger signal has been sent to the camera. Image data must be acquired, transmitted, and evaluated within a set time-frame. The requirements for the maximum acceptable reaction time between trigger signal and image acquisition can vary from microseconds up to seconds. For industrial cameras, real-time images are a prerequisite. When inspecting components in a production process, for example, the components are transported on the conveyor belts at high speed. For a precise inspection, the camera must acquire the images as quickly as the components are being transported. Timing this precisely requires low latency: a small time delay between receiving the trigger signal and acquiring the image. Further, the time delay must not vary; no jitter can affect the moments of image acquisition. For an application with high image rates (e.g. 300 images per second), the required latency times can only be microseconds.

Similar requirements also exist outside of the factory, especially in traffic applications. In speed control systems, the camera activity must be synchronized with other system components, such as illumination devices. Many classic IP cameras are not real-time capable. In typical surveillance situations, such as monitoring the activity on a banking floor, the user needs an automatically captured, continuous stream of images without the need to trigger a camera. If a camera is set for a frame rate of 30 frames (images) per second, it will internally generate the signals required to initiate an image capture every 1/30th of a second. In some other situations, however, it would be desirable to be able to trigger an image capture at a specific point in time. For example, in a traffic control situation the user may want to trigger an image acquisition immediately after a car passes a sensor on a highway.

Converging the Cameras

Despite the different technologies and objectives, many applications are compatible with both types of cameras. There are now even solutions that operate IP cameras and GigE industrial cameras in one single Ethernet-based system using the same software.

The setup is already daily practice in the paper industry. Similar concepts are also in place for steel and foil production. Equipment used for these tasks typically involves numerous sequential process steps spread out over a physical space, including multiple sub-systems that need to pass products like paper webs to one another. IP cameras monitor the individual process steps and the transfer of the product to the next machine. The network devices ensure optimal interplay between all equipment systems, identify threats and sources of disruption, initiate a production stop where necessary, and generally help to optimize the machine configurations. If, for example, problems tend to arise in a given location, then the engineers will know to give that area a closer look. At the same time, classic cameras operate within the individual inline systems and test for quality, completeness, or dimensions.

Production robots in the automotive industry often combine both camera types: small and light industrial cameras inside the robots, and network cameras for process control.

Additionally, in the world of robotics, machine vision systems have long been used to “teach robots to see.” After all, robot arms can only perform their high-precision gripping and positioning maneuvers once cameras and image processing tell them precisely where to move. Small, light industrial cameras, like the Basler ace, are typically used in robots. The devices are ideal for “pick and place” applications: gripping, mounting, and positioning tasks during computer chip assembly. Industrial cameras are also needed for measurement and quality controls of the various product characteristics between and during all production steps.

Safety during the production process is a prime concern for robotics applications. Previously, barriers were built to prevent workers from entering into the danger zones containing the rotating robots. The structures were expensive, cumbersome, and inflexible. IP cameras have changed the situation fundamentally. The network technologies can now be placed around the robots to create a “virtual cage” based on markings on the floor. The camera monitors those marked zones and is authorized to stop the machine if material, or an employee, enters into the robot’s working area.

In daily use, the boundaries between the two camera technologies are largely becoming blurred. IP cameras, which classically have served in the monitoring field, are more and more frequently being used in industrial contexts to support process monitoring and production workflows. Camera manufacturers are also offering customers a range of cameras and accessories to ensure an optimal interplay between all components.

This article was written by Eva Tischendorf, Senior Communications Specialist at Basler AG (Ahrensburg, Germany). For more information, Click Here .

Imaging Technology Magazine

This article first appeared in the June, 2014 issue of Imaging Technology Magazine.

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