GPLG · INDUSTRIAL VISION
Machine Vision Technologies
Industrial vision systems for inspection, detection, measurement and production intelligence.
GPLG integrates cameras, optics, lighting, image processing and industrial software around real production requirements. A reliable machine vision system begins with the physical image-acquisition conditions and ends with a result that the automation system can use.
SEE THE PROCESS
Turn industrial images into useful production information
Machine vision can inspect characteristics that conventional sensors cannot evaluate efficiently: presence, position, dimensions, orientation, surface conditions, markings and other visible features.
Performance depends on more than the algorithm. Camera position, lens, lighting, exposure, triggering, part presentation and representative samples determine whether the image contains the information required for a robust decision.
The quality of the vision result starts with the quality and repeatability of the acquired image.
VISION CAPABILITIES
Inspection functions around the production process
Presence & assembly
Verify that required components or visible assembly features are present.
Position & orientation
Locate parts and determine their position or orientation for downstream operations.
Measurement
Evaluate suitable dimensions and geometric features using calibrated vision methods.
Inspection & classification
Detect or classify defined visual conditions using conventional or AI-assisted processing.
REAL-TIME INDUSTRIAL INSPECTION
Camera, lighting, processing and automation working as one system
The inspection result becomes useful when image acquisition is synchronised with the production process and integrated with PLC, machine control or higher-level software.
VISION ARCHITECTURE
From the physical scene to an industrial decision
A maintainable machine vision system separates acquisition, processing and automation responsibilities while preserving the timing and context required by the machine.
MACHINE VISION FLOW
IMAGE ACQUISITION
Engineer the image before engineering the algorithm
Camera
Select sensor, resolution, frame rate and interface around the inspection requirement.
Optics
Choose field of view, working distance and lens characteristics appropriate to the feature.
Lighting
Create repeatable contrast and suppress unwanted reflections or environmental variation.
Triggering
Synchronise image capture with machine state, product position and production speed.
VISION ON THE PRODUCTION LINE
Inspection must operate within the real machine cycle
Acquisition, processing time, communication and reject or handling logic are engineered together so the vision result arrives where and when it is required.
CLASSICAL VISION
Deterministic image processing for well-defined features
Traditional machine vision methods remain effective where features can be described through contrast, geometry, edges, patterns, colour or calibrated measurements.
These approaches can provide transparent and repeatable inspection logic when the physical presentation of the product is sufficiently controlled.
AI-ASSISTED VISION
Use learned models where visual variation requires a different approach
Machine-learning methods can be evaluated when the required visual distinction is difficult to express through fixed rules. Suitable applications may include classification, detection or anomaly-related tasks.
The model must be validated against representative production conditions, including difficult and failure cases. AI does not remove the need for good optics, lighting or engineering controls.
INDUSTRIAL INTEGRATION
Make the vision result actionable
PLC integration
Exchange triggers, recipes, status and inspection results with machine control.
Robot guidance
Provide position or orientation information where the validated application requires it.
Traceability
Associate selected images and results with production records when required.
Operator interface
Present status, diagnostics and rejected-image context clearly to production personnel.
FROM SAMPLE TO COMMISSIONING
A structured machine vision engineering process
Application assessment
Define the feature, tolerance, cycle time and required machine response.
Image feasibility
Evaluate samples, presentation, optics and lighting to establish whether the feature can be seen reliably.
Vision development
Implement acquisition, processing, measurement or model logic for the validated approach.
Machine integration
Connect triggering, recipes, results and diagnostics to the automation environment.
Production validation
Test representative good, bad and difficult conditions at realistic operating speed.
Support & evolution
Adapt the system as products, tooling, presentation or inspection requirements change.
CONNECTED ENGINEERING
Machine Vision within the wider GPLG environment
PLG AI
AI-enabled analysis and decision support for industrial applications.
Explore →Python Development
Industrial software, OpenCV and data-processing workflows.
Explore →PLC Programming
Machine control and deterministic integration of inspection results.
Explore →Industrial IoT
Connected production data and higher-level system integration.
Explore →MACHINE VISION
Need to inspect, detect or measure something on your production line?
GPLG can assess the visual requirement, production conditions and automation interface before defining the appropriate vision architecture.