Laser spot positioning and beam profile analysis algorithm
The laser spot positioning and beam profile analysis algorithm is oriented to laser module assembly, optical axis return and beam shape inspection, and converts the spot in the camera image into center position, width, axial ratio, relative distribution and effectiveness results. The applicable premise is that the target light point is separable within the field of view, and the exposure, lens and reference coordinates are clear.
Project Judgment Summary
Process tasks and input
Recording only the brightest pixels is susceptible to noise, saturation, and beam shape changes, and cannot account for center shift before and after the frame is locked. This algorithm separates positioning, beam shape and quality diagnosis, so that position changes and image abnormalities can be reviewed separately.
Input requirements: original single frame or continuous frame, exposure and gain, ROI, pixel format and band response; when outputting millimeter coordinates, the scale and calibration data of the working plane must also be corresponding.
| Function | processing content | current base |
|---|---|---|
| Light spot center positioning | Select the grayscale centroid, local fitting or radial positioning method according to the light point and background conditions, and output the image coordinates; the methods must be compared on the same independent sample set. | Already have calculation reference implementation |
| Spot shape analysis | Outputs reference quantities such as second-order moment width/axis ratio and surrounding energy, while marking saturation, truncation, and low signal-to-noise conditions. | Already have calculation reference implementation |
| Return and drift statistics | Statistics of front and rear center differences, repeatability and timing trajectories are performed under the same reference system, and invalid frames are retained instead of only showing smooth curves. | Repeated calculations have been made; the cause of equipment drift needs to be verified separately. |
Output content
| result | unit or form | Instructions for use |
|---|---|---|
| Center to Reference Deviation | px; mm after calibration on the effective plane | Returns the coordinate system, ROI and calibration version at the same time |
| Spot width, axial ratio and relative distribution | px/dimensionless; physical scale is calibrated separately | Relative gray scale is not used as absolute optical power |
| Quality and repeatability | Valid states, spreads, and original sequences | No point, occlusion, saturation or truncation independent markers |
Applicable conditions and boundaries
Applicable tasks: Fixed target surface light spot, light output head that can sample attenuation, installation adjustment and beam shape comparison under the same conditions.
It does not directly measure absolute power, M², wavefront or three-dimensional position of the target; changes in the two-dimensional center are not necessarily the translation of the device under test. The camera spectral response, optical path changes and reference frame drift need to be checked separately.
Verification basis and delivery scope
The 2026-10-24 special record contains 7 combinations of synthetic light spots and controlled geometric inputs, and 12 special benchmark passes, used to confirm calculation behaviors. Physical accuracy, processing time, and long-term stability acceptance of cameras, lenses, platforms, or product operating conditions have not been specified.
Adapt the collection/playback interface, algorithm call, parameter formula, coordinates and abnormal status, calibration files and report export according to the project. The current Python reference implementation is not equivalent to the SDK that has been formed for all platforms; the binary, source code and running environment are agreed separately.
Requirement confirmation information: Provide normal and abnormal original frames, spot size, working distance, wavelength/camera response, target output unit and allowable error.
FAQ
Can sub-pixel positioning directly promise millimeter accuracy?
No. Physical errors are also affected by imaging ratio, lens, exposure, light path and calibration, requiring independent position true value verification.
Do I need to train a model?
The current core is mainly geometric and grayscale calculations, and model training is not added by default just because the software is delivered; if the scene really requires a learning module, it will be confirmed separately.
Enterprise project promotion and acceptance
- scope confirmationClarify the objects, workstations, inputs, outputs, units, interfaces and abnormal conditions that must be covered.
- Sample verificationUse representative normal and abnormal samples to establish a baseline and record what works, what fails, and data gaps.
- Development joint debuggingComplete the algorithm, calibration, interface and equipment adaptation according to the approved scope, and retain the version, parameters and abnormal status.
- Item acceptanceUse independent samples and reference value retests to confirm errors, detection, tempo, stability and deliverables respectively.
Define the Development Scope with Samples and Device Requirements
Laser spot positioning and beam profile analysis algorithm
Please describe the task object, original picture or video, target indicators, device platform, interface and sample situation.
Phone: 13910119357xuzhiyang0928@gmail.com