In the construction of industrial vision systems, many people tend to overlook a crucial parameter—pixel format. Little do they realize that this parameter directly affects image storage efficiency, color reproduction accuracy, and data processing workload. Choosing the right pixel format can double detection efficiency; selecting the wrong one, however, may lead to false positives and missed detections.

I. First, let’s understand: What exactly is a pixel format?

Simply put, a pixel format refers to how an industrial camera captures images.Data storage and organization rules for each pixel.It’s like “the way image data is packaged”—different packaging methods determine the size of the data, the information it contains (grayscale or color), and the difficulty of subsequent processing.

The core value of industrial cameras is “accurately capturing meaningful information,” and the pixel format directly determines and filters “which information will be captured”: For example, if you only need to determine whether a part has defects, there’s no need to capture color information; but if you need to distinguish between colored materials, you must choose a format that can faithfully reproduce colors. The common pixel formats used in industrial cameras fall into four main categories: Mono (monochrome), Bayer, RGB, and YUV.

II. Four Common Pixel Formats: Features, Differences, and Applicable Scenarios

The core differences among various pixel formats lie in whether they include color information and how they store color information—these differences also determine their respective application scenarios. Let’s break them down one by one:

1. Mono Format: The “Efficiency King” of Monochrome Imaging

Mono format, or monochrome (grayscale) format, is the mainstream choice for industrial black-and-white cameras. Its core feature is...Each pixel stores only brightness information (grayscale value) and does not contain any color information.For example, Mono 8 means each pixel is stored using 8 bits, with a grayscale range of 0–255 (where 0 represents pure black and 255 represents pure white); Mono 10, on the other hand, uses 10 bits for storage, with a grayscale range of 0–1023, offering even greater detail.

Core advantagesIt has the smallest data volume, the highest storage and transmission efficiency, and the fastest camera frame rate; it is less sensitive to changes in lighting and exhibits strong detection stability.

Applicable scenariosDetection tasks that do not require color differentiation—such as part dimension measurement, surface defect detection (scratches, cracks, missing material), and barcode reading—can be handled effectively. For example, in a 3C product border dimension inspection project, using the Mono 8 format allows the camera to achieve a frame rate of up to 300 FPS, significantly higher than that of color formats, making it an ideal match for high-speed production line rhythms.

2. Bayer format: the “raw data format” for color cameras

The Bayer format is the “native format” for color industrial cameras. Its core design principle is to acquire color information using the least amount of data possible. A Bayer filter array—commonly arranged in GR/GB/BG patterns—is overlaid on the camera sensor.Each pixel records only one of the three colors: red, green, or blue.The information for the remaining two colors needs to be calculated through interpolation of the values from adjacent pixels.

Core advantagesThe data volume is significantly smaller than that of the RGB format (and comparable to the Mono format), allowing it to maintain a certain level of color recognition capability while also optimizing frame rate and storage efficiency.

LimitationColor accuracy depends on the interpolation algorithm, which may result in slight color deviations. In edge regions, false colors may appear.

Applicable scenariosFor color detection tasks that do not demand high color accuracy—such as material color sorting (distinguishing between red, blue, and green packaging) or determining whether the appearance color of a product deviates from the standard—consider, for example, a food packaging sorting line that uses a color camera in Bayer format to differentiate between packages of various flavors. This approach not only meets the detection requirements but also helps keep data processing demands under control.

3. RGB Format: The “King of Color Image Restoration”

RGB format is the standard color format.Each pixel contains complete information for the red (R), green (G), and blue (B) channels.It requires no interpolation calculations and can most accurately reproduce image colors. The common RGB 24 format allocates 24 bits per pixel (8 bits per channel), offering an extensive color gamut and extremely high color fidelity.

Core advantagesAccurate color reproduction and rich detail make it ideal for scenarios requiring precise color analysis.

Limitation: The data volume is the largest (three times that of the Mono 8 format), consuming significant storage and bandwidth, which can lead to a drop in camera frame rate and increase the processing load on subsequent algorithms.

Applicable scenariosFor tasks that demand extremely high color accuracy—such as textile color difference detection, cosmetic appearance color grading, and print color calibration—take, for example, a high-end garment fabric inspection project. In such cases, it is essential to use the RGB 24 format to precisely distinguish even the slightest color differences on the fabric and prevent substandard products from being released.

4. YUV Format: The “Efficient Choice” for Video Processing

The YUV format is a format specifically designed for video transmission and processing. Its core advantage lies in “separating luminance and chrominance information”:Y represents luminance (grayscale) information, while U and V represent chrominance (color) information.Since the human eye is more sensitive to changes in brightness than to changes in chrominance, the YUV format can compress data volume by “reducing the sampling rate of chrominance information” while ensuring that the visual quality remains largely unaffected.

Common YUV subsampling formats include YUV 422, YUV 444, and YUV 420: The larger the number, the more complete the chrominance information, but also the greater the data volume (YUV 444 ≈ RGB 24; YUV 422 ≈ two-thirds of RGB 24; YUV 420 ≈ half of RGB 24).

Core advantagesThe data volume is smaller than that of the RGB format, while its color reproduction accuracy is comparable to RGB, striking a balance between efficiency and visual quality. The separation of luminance and chrominance enables more efficient subsequent image processing tasks, such as edge detection and object tracking.

Applicable scenariosIndustrial scenarios that require dynamic video analysis include dynamic workpiece tracking on assembly lines, defect detection in moving objects, and industrial surveillance. For example, in a dynamic tracking project for an automotive parts assembly line, the YUV 422 format was chosen to ensure both excellent color recognition capabilities and smoother video transmission and processing.

III. Key Supplement: The Relationship Between Pixel Format and Packet

When discussing pixel formats, you’ll often come across the concept of “packet” (packing). Its core function is:Optimize storage space and avoid waste..

Without packets, cameras typically store pixel data in fixed-size memory spaces (e.g., 16 bits). For example, in the Mono 10 format (10 bits per pixel), if the data is stored directly, it would occupy 16-bit memory, leaving 6 bits unused. In contrast, the Mono 10 Packet format stores the 10-bit data compactly in a 12-bit memory space, wasting only 2 bits and significantly improving storage and transmission efficiency.

Practical recommendation: In scenarios where bandwidth or storage is limited (such as high-speed detection or long-term continuous data acquisition), prioritize pixel formats that include the term "Packet" to minimize data waste.

IV. Comparison Across Four Key Dimensions: Quickly Selecting the Right Pixel Format

To facilitate quick selection, we compare the four formats across four core dimensions: “pixel information, data volume, frame rate, and imaging quality.”

Pixel informationMono (grayscale only) < Bayer (single-channel color + interpolation) < YUV (luminance + chrominance separation) < RGB (three-channel full color);

Data volumeMono ≈ Bayer < YUV (420/422) < YUV 444 ≈ RGB;

Frame rateMono > Bayer > YUV > RGB (for the same camera, the smaller the data volume, the higher the frame rate);

Imaging performanceRGB (color accuracy) ≈ YUV 444 > YUV 422 > Bayer (slight color deviation); Mono (grayscale with sharp details, no color).

V. Practical Guide: How to Set the Pixel Format

The steps for setting the pixel format are simple, but there’s one crucial prerequisite:You must first stop the camera's image acquisition stream.Otherwise, the parameters cannot be modified. The specific steps are as follows:

1.Open the camera control software (such as Halcon, LabVIEW, or the software provided by the camera manufacturer) and connect to the target industrial camera.

2.In the software’s “Camera Parameters” or “Property Tree,” locate the “Pixel Format” option.

3.First, click the “Stop Acquisition” button to ensure that the image acquisition stream has been interrupted.

4.In the Pixel Format drop-down menu, select the desired format (for example, choose Mono 8 to detect part defects, or choose Bayer GR8 to sort colored materials).

5.Click “Start Collection” to verify whether the image quality meets the requirements. If it doesn’t, repeat steps 3–4 to make adjustments.

Notes: Pixel formats supported by different camera manufacturers may vary slightly. For example, some cameras support formats such as Mono 12 and RGB 32. When selecting a camera, you should determine the appropriate format based on the camera’s specifications and your inspection requirements.

Finally, the core logic behind selection is “matching needs.”

To sum up in one sentence: When choosing a pixel format, don’t strive for “the most advanced”—just make sure it “meets your needs.”

Remember these three core principles: ① No need for color—prioritize Mono (highest efficiency); ② If simple color differentiation is required, choose Bayer (balancing efficiency and cost); ③ If precise color accuracy or dynamic video analysis is needed, choose RGB or YUV (select the sub-sampling format based on data volume requirements).

Once you’ve mastered this logic and combined it with practical setup methods, you’ll be able to easily select and configure the pixel format for industrial cameras, making your vision system more efficient and stable.