What should I pay attention to when selecting industrial cameras and lens?

In machine vision systems, industrial cameras and lenses work together as the camera's "eyes," carrying the crucial task of transmitting external optical information to the sensor. Lens performance and optimal parameter selection directly determine image quality and inspection accuracy. Therefore, mastering the basics of lens selection is crucial for improving the efficiency of industrial inspection and automated production.

Basic Concepts of Industrial Camera Lenses

Industrial lenses differ from standard consumer lenses in that their primary goal is to ensure accurate, stable, and consistent imaging. In image measurement processes and machine vision applications, they are often used for precision measurement, defect detection, and dimensional identification. Compared to the performance of consumer lenses, industrial lenses prioritize low distortion, high resolution, and imaging uniformity to ensure stable image data in large-scale production environments.

Camera and Lens Selection

When selecting a lens, it's crucial to consider the compatibility of camera parameters with inspection requirements. A camera's resolution, target surface size, and pixel size are all closely related to the lens' focal length, magnification, and aperture range. A mismatch between the lens and camera can result in incomplete imaging, loss of resolution, or excessive distortion, impacting the accuracy of subsequent inspection and recognition.

How can industrial cameras ensure accuracy and stability?

The stability of industrial lenses primarily stems from the following factors:

Low distortion: Ensures that the geometric shape of the object image on the sensor matches the actual image;

High resolution and uniformity: Ensures clear images across the entire field of view, without noticeable edge blur;

Proper optical design: Optimizes aperture, focal length, and depth of field to achieve stable images even in complex lighting conditions;

Matched mount and sensor size: Avoids vignetting and optical loss, ensuring a perfect fit between the lens and camera.

Lens Distortion

Distortion is a key parameter to consider during lens selection, as it directly impacts measurement and inspection accuracy.

Radial distortion: Distortion distributed along the radius of the lens. Since light bends more significantly as it moves away from the center of the lens, common forms include:

Barrel distortion: The image edges expand outward, appearing like a bulge;

Pincushion distortion: The image edges contract inward, appearing like the edge of a pillow.

Tangential distortion: Caused by decentration during lens installation or manufacturing, it manifests as a tangential shift of the image point relative to its ideal position.

Different types of distortion can lead to inspection errors, so in precision measurement scenarios, it is important to prioritize industrial lenses with excellent distortion control.

Distortion manifests in various forms.

In addition to barrel and pincushion distortion, compound distortion may also occur in practical applications. In these cases, correction through calibration or software algorithms is necessary. The complexity of distortion requires users to consider not only lens specifications but also actual application environments when selecting a lens.

Common Camera Calibration Methods

To eliminate the effects of distortion and ensure image consistency with real-world coordinates, camera calibration is often required. Common calibration methods include:

Traditional Camera Calibration

Using a calibration object of known size (such as a chessboard), the correspondence between calibration points and image points is established to calculate the camera's intrinsic and extrinsic parameters. Depending on the calibration object, this method can be categorized as planar calibration or three-dimensional calibration. While this method offers high accuracy, it requires high-quality calibration objects and is relatively cumbersome.

Active Vision Camera Calibration

This method does not rely on calibration objects and instead infers camera parameters based on the known motion trajectory. Its advantages include a simple algorithm and good robustness, but its disadvantages are expensive experimental equipment and its applicability only to scenes with controlled motion.

Camera Self-Calibration

Calibration is performed based on geometric features in the scene (such as parallel and orthogonal lines). Camera parameters are inferred from vanishing points and projective geometric relationships. This method is flexible and suitable for online calibration, but it is complex and lacks robustness.

Selecting industrial camera lenses involves many aspects, including optical principles, imaging parameters, and calibration methods. Only by thoroughly understanding parameters such as focal length, distortion, depth of field, and aperture, combined with practical application scenarios, can the most appropriate lens be selected. For machine vision systems striving for high precision and reliability, proper lens selection and appropriate calibration methods are crucial for ensuring inspection quality and production efficiency.