Frame Rate in Industrial Cameras

Frame rate is the frequency at which an industrial camera captures and transmits distinct images, typically measured in frames per second (FPS). In machine vision applications, frame rate is an important factor in determining whether the imaging system can keep pace with the speed of the production process.

For example, if a conveyor presents 50 parts per second to the inspection station, the camera system may need to acquire, transfer, and process images at a corresponding rate to maintain reliable inspection coverage. Achieving higher throughput often requires balancing sensor resolution, exposure time, processing requirements, and interface bandwidth.

The Physics of Camera Speed

Frame rate is influenced by several stages within the image acquisition pipeline, including exposure, sensor readout, and data transfer.

The total time required to produce a single image depends largely on the exposure time (the duration the sensor collects light) and the sensor readout time (the time required to convert and transfer pixel data from the sensor). Longer exposure times can improve image quality in low-light conditions, but they also reduce the maximum achievable frame rate.

For example, if an application requires a 20-millisecond exposure to maintain an acceptable signal-to-noise ratio, the achievable frame rate may be limited to approximately 50 FPS before additional sensor readout or transfer overhead is considered.

The Interface Bandwidth Bottleneck

After image data leaves the sensor, it must be transferred to a host PC or embedded processing platform. In high-resolution or high-frame-rate applications, the available interface bandwidth can become an important limiting factor.

High-resolution sensors generate large amounts of image data for each frame. If the interface bandwidth is insufficient to transfer that data continuously, the camera may need to reduce its frame rate or rely more heavily on internal buffering.

Interface Standard

Practical Bandwidth

Impact on Frame Rate

GigE Vision (1 GigE)

~115 MB/s

Well suited for long cable runs and distributed industrial systems; may limit achievable frame rates at very high resolutions

USB3 Vision

~400 MB/s

Widely used for high-bandwidth industrial imaging applications requiring higher frame rates

MIPI CSI-2

Scales by lane (e.g., ~1.25 GB/s on 4 lanes)

Designed for direct board-level integration in embedded systems with high-bandwidth, low-latency image transfer

How to Increase Frame Rate Without Changing Hardware

If an imaging system is limited by interface bandwidth or processing throughput, several camera configuration techniques can help increase achievable frame rates without changing the hardware platform.

1. Region of Interest (ROI)

Instead of reading the entire sensor area, the camera can be configured to acquire only a selected subset of pixels. For example, if the inspection task involves a small barcode or feature near the center of the image, reducing a 5 MP image to a smaller ROI significantly lowers the amount of transmitted image data.

Because fewer pixels must be read, transferred, and processed, ROI configuration can often increase achievable frame rates while preserving the sensor's native spatial resolution within the selected region.

2. Binning / Decimation

If maintaining the full optical field of view is important, higher frame rates can also be achieved through binning or decimation.

Binning combines adjacent pixels to reduce image resolution while improving light sensitivity and reducing data volume. Decimation reduces resolution by skipping pixels during sensor readout. Both methods reduce bandwidth requirements and can increase frame rate, though at the cost of reduced spatial detail.

Frame Rate vs. Exposure Time: What Is the Tradeoff?

Increasing frame rate often requires shorter exposure times, particularly in applications where the sensor must complete image acquisition within a limited time window.

Shorter exposures allow less light to reach the sensor, reducing the total photon count collected for each frame. As exposure time decreases, image brightness and signal-to-noise ratio may also decrease, especially in low-light conditions.

For example, increasing the frame rate from 60 FPS to 120 FPS reduces the maximum available exposure time for each frame, which can affect image quality if illumination conditions remain unchanged.

To maintain adequate image contrast and measurement reliability at higher frame rates, machine vision systems often use higher-intensity illumination or synchronized strobe lighting to deliver sufficient light during the shorter exposure interval.

Frequently asked questions

Dropped frames occur when the camera generates data faster than the host system can process it. This is rarely a camera failure. It typically happens when the interface bandwidth is saturated, the network switch is overloaded (in GigE setups), or the host PC's CPU cannot process the incoming image buffers fast enough.

It depends on where the processing happens. If a color camera outputs raw Bayer data (typically 8-bit), the bandwidth requirement is exactly the same as a monochrome camera. However, if you configure the camera to perform the debayering on-board and output a processed RGB24 format, you are tripling the data size per pixel over the cable. That massive bandwidth spike will severely lower your maximum frame rate.

Not always. A USB 3.1 camera might successfully capture and transmit 200 FPS, but if your machine vision software takes 15 milliseconds to run a complex algorithm on a single frame, without hardware pipelining or multi-threaded buffering, your effective throughput is capped at ~66 FPS. Your camera's capture rate must ultimately align with your system's processing reality.

Glossary