How To Choosing a Smart Camera

Smart cameras are the most effective means of rapidly implementing simple machine-vision systems due to their reliability, cost effectiveness and easy to integration. As a self-contained unit, Smart cameras can be used for embedded single unit applications or networked to industrial manufacturing systems.

Below is a breakdown of the basic components of a smart camera and some important considerations when choosing a camera for your application.

Basic smart camera architecture:

What are the parts and specific considerations?


An image sensor is an integrated circuit that converts a visual image into an electrical signal. There are several different components to consider when selecting a sensor to fit the given application. The basic parameters for smart camera sensors are type, resolution, pixel size, frame rate, and shutter types

Type: two types of sensors commonly used in machine vision: charge-coupled device (CCD) and complementary metal-oxide-semiconductor (CMOS). Both sensors capture light using pixels to digitize the information.

CMOS imagers offer superior integration, power dissipation and system size at the expense of image quality (particularly in low light) and flexibility. They are the technology of choice for high-volume, space constrained applications where image quality requirements are low. This makes them a natural fit for security cameras, PC videoconferencing, wireless handheld devices, bar-code scanners, fax machines, consumer scanners, toys, biometrics and some automotive in vehicle uses.

CCDs offer superior image quality and flexibility at the expense of system size. They remain the most suitable technology for high-end imaging applications, such as digital photography, broadcast television, high-performance industrial imaging, and most scientific and medical applications. Furthermore, flexibility means users can achieve greater system differentiation with CCDs than with CMOS imagers.

Resolution: This refers to the number of pixels in the sensor region. Resolution and zoom ability grows in direct proportion to the number of pixels in the sensor. However, higher resolution images require more processing power and large data lines. So when evaluating resolution needs it is important to correctly determine the necessary level of clarity to complete the application.

Pixel size: Overall pixel size affects the sensors light sensitivity. The larger the pixel the more sensitive to light it will be. However other factors contribute to this equation, including ambient light, projected light and of course what is being imaged.

Frame rate: This refers the amount of frames that can be captures in a given second. A standard rule is that the greater the resolution the lower the frame rate, however, it is also a function of the size of the image. Smart cameras have a frame rate advantage because the onboard processor can predetermine fields of interest and only capture a small part of each image.

Shutter types: There are two ways in which the sensor is exposed to light: global and rolling. Rolling exposes each line of the sensor successively over time, which is an overall simpler method and ends up significantly less expensive. But the rolling method can distort objects moving at high speed and is therefore unsuitable for some applications. Global shutters expose the pixels all at once, taking a complete shot of the field of view. Although ideal for high speed applications, Global shutters are inherently more expensive and sometimes require larger processing power to manage the large amount of data.


The heart of any smart camera system is the digital signal processor (DSP), and the storage hardware. The DSP processes images by executing algorithmic programs designed to interpret digital image code. Storage hardware is the primary and secondary memory, such as RAM or Flash, used to run CPU programs, or to record and store images for future use. DSPs are quite complex but there are two main considerations for selection, processing capabilities and manufacturer. The higher the processing power, the more expensive the device will typically be. When deciding on manufacturers look at reviews and overall market presence because the chips durability and the reliability depend solely on the manufacture. The more established companies will be able to provide more support and guidance when using their products.


I/O capabilities connect cameras to external devices transmitting encoded images to a computer for analysis, or delivering instructions to reactive equipment. Smart camera I/O capabilities are expanding and now include Ethernet (GigE), USB 2.0, Bluetooth, Wi-Fi, and GPRS.

Ethernet: Most common I/O solution can handle large amounts of data, but can be expensive and is not compatible with all computers.

USB2.0: Simple and easy to use solution that is universally compatible and inexpensive. Although many vendors do not see USB2.0 as capable enough, many machine builders looking for inexpensive yet high performance cameras are turning to USB2.0 to solve their price performance dilemma.

Blue tooth, Wifi and GPRS: All of the non wired solutions are practical for remote location application and represent a new field of smart camera capabilities. But they are limited to low bandwidth applications and are often very expensive to produce.


While some camera vendors support just one operating system, a number have chosen to support one or more operating systems in their products. The reason for such multiple operating system support is clear. Doing so allows these camera vendors to rapidly port existing PC-based software to their products. At the same time, developers of lower-cost open sourced systems can take advantage of the number of free software packages and development tools currently available.

In this manner, companies can offer their cameras with a range of application software while reducing the developer’s time to market. Third-party software developers can also add their own functionality to the cameras, offering unique products that serve specific markets.

Of course, with added programming complexity comes the need to offer the integrator a set of easy-to-configure machine-vision tools that require little or no programming expertise.

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