Technical guides
Concepts and selection criteria for designing industrial machine vision systems, from choosing the camera, interface and lens to the lighting and inspection software.
How to choose an industrial camera
A practical, application-first method for selecting an industrial camera: derive resolution from field of view and smallest detail, then match shutter, speed, interface, lens and lighting.
Understanding resolution and field of view
How to size camera resolution from field of view and the smallest defect using the 3-5 px rule, plus why optics, lighting and bandwidth cap usable resolution.
GigE Vision vs USB3 Vision vs CoaXPress
A practical decision guide to the three dominant machine-vision interfaces: bandwidth, cable length, multi-camera support, PoE, latency, frame grabber needs and cost.
Area scan vs line scan cameras
When to use a matrix (area scan) sensor versus a line sensor: discrete parts vs continuous web and cylindrical material, encoder sync, lighting and frame grabber needs.
Global shutter vs rolling shutter
How global and rolling shutters differ, why rolling shutter distorts moving objects, and when each is the right choice for industrial inspection.
Monochrome vs color cameras
When to choose monochrome vs color machine-vision cameras: Bayer light loss and effective resolution, color-dependent tasks, wavelength-specific lighting, NIR sensitivity and processing cost.
High-resolution imaging for inspection
When 20+ MP cameras earn their place in inspection: sensor formats, the optics and MTF challenge, bandwidth and interfaces, lighting uniformity, and single high-res vs multi-camera.
High-speed imaging for inspection
How to build a high-speed machine-vision system: balance frame rate against resolution, freeze motion with short exposure and strobe lighting, and size the interface and frame grabber for sustained throughput.
How to choose a lens for machine vision
A practical engineer's guide to selecting machine-vision lenses: focal length from FOV and working distance, sensor format, resolving power, aperture, depth of field and telecentricity.
How to choose machine vision lighting
A practical guide to choosing machine vision lighting: geometry, wavelength, strobe vs continuous, ambient control and how to maximise feature contrast for reliable inspection.
Smart camera vs PC-based vision
How to choose between all-in-one smart cameras and PC/embedded vision with a separate camera, based on inspection complexity, throughput, flexibility, AI needs and total cost of ownership.
Industrial OCR and OCV
OCR reads text; OCV verifies it against a reference. The deciding factor is stable, high-contrast imaging — and where classic methods fail, deep-learning OCR.
Types of machine vision lighting: ring, backlight, coaxial, dome and dark field
A practical guide to machine vision lighting geometries — ring, bar, backlight, coaxial, dome and dark field — explaining what each reveals, the surfaces it suits, and how to choose the right one directly from your inspection problem.
Wavelength and color in machine vision lighting: white, red, blue, green, IR and UV
A practical engineering guide to choosing LED wavelength in machine vision lighting. Learn how monochromatic red, blue, green, IR and UV light change contrast, how to exploit complementary colors, and how to match wavelength to your camera, lens and optical filters.
Strobe vs continuous lighting and how to choose a lighting controller
A practical guide to continuous, strobed, and overdrive LED lighting in machine vision: why a short pulse freezes motion, why overdrive (not strobing alone) adds brightness, how to synchronize the pulse to camera exposure, the duty-cycle limits that protect LED life, and how to size and select a lighting controller.
How to choose an industrial sensor for vision triggering and part detection
A practical guide to selecting discrete sensors that trigger cameras, index parts and gate inspection on a vision station. Covers sensing principle, range, target, PNP/NPN/IO-Link outputs, response time and IP rating, plus wiring the trigger to the camera.
Photoelectric sensor types explained: through-beam, retroreflective, diffuse and background suppression
A practical guide to the core photoelectric sensing principles, through-beam, retroreflective, diffuse and background suppression, covering range, reliability, target and surface dependence, light source choice, output options and how to pick the right one for your application.
Laser displacement and distance sensors: triangulation, confocal and time-of-flight
A practical guide to laser displacement and distance sensors: how triangulation, confocal and time-of-flight measure distance, height and profile, how resolution trades against range and target reflectivity, and when a dedicated measurement sensor beats a vision system.
How to choose a 2D and DPM industrial code reader
A practical engineering guide to selecting image-based 1D, 2D and DPM code readers: how to match resolution and field of view to module size, set working distance and depth of field, pick DPM lighting and optics, and choose decode strategy, communication and grading.
Code verification and grading: ISO/IEC and GS1 for traceability
A practical guide to barcode and Data Matrix verification: how grading differs from reading, the ISO/IEC 15415 and 15416 parameters, ISO/IEC 29158 for direct part marks, GS1 application rules, and why pharma and medical packaging demand verified codes.
Optical filters for machine vision: polarizers, bandpass and IR-cut
How optical filters raise contrast and robustness in machine vision: polarizers to kill glare and reveal stress, bandpass filters matched to monochromatic lighting to reject ambient light, IR-cut and IR-pass, and neutral density. Selection, pairing with lighting, and mounting.
How to validate a machine vision application: a practical checklist
A field checklist to de-risk a machine vision project before you buy hardware: define defects and tolerances, collect representative samples, prove the imaging, budget resolution and field of view, lock motion and triggers, then run a structured proof of concept against measurable acceptance criteria.