Design the lighting before you touch a camera
IR to overpower ambient without operator eye strain; diffused always-on versus strobed, chosen to the surface texture and reflectivity. Lighting is not a peripheral — it is the first specification.
Image Acquisition Expertise
The image is the crucial part of machine vision
Software tuning cannot rescue an image with glare, no contrast, variable ambient light, or an object outside the focal plane. Qualitas starts with the image, not the algorithm — because getting the image right is the only fix that actually holds.
Why vision systems fail
Most failures are imaging faults — not software bugs
The most common failure mode in machine vision is ambient light drifting across a production shift. As overhead lighting cycles, reflections change, and shadow angles shift — the detection model that passed at 8 a.m. starts generating false rejects by 2 p.m. The engineering team chases a software bug that does not exist.
The same causal chain runs through every imaging failure: glare that bleaches surface texture, insufficient contrast between defect and background, variable working distance pushing features out of the focal plane. These are lighting and optics problems. No algorithm — no matter how large or well-trained — recovers detail that was never captured.
Get the image right and the model tunes easily. Get it wrong and no software saves you. Qualitas engineering starts with the image.
Two disciplines, one rig
Optical engineering paired with mechanical engineering
Every engagement pairs an optical/imaging engineer with a mechanical/automation engineer — because a perfect lens specification fails if the mount introduces vibration at line speed. This pairing is what makes our turnkey offering (Material Handling + Image Acquisition + Software) real rather than a slide: the rig is designed as a single system, not three parts handed off between teams.
What we engineer
Five decisions that determine whether the image works
Optics that hold focus as the object moves
Liquid focal lenses for varying working distance and depth of field within a single inspection cycle — so a part that tilts or varies in height stays sharp across the full frame.
The right sensor, not the biggest one
Area versus line scan, resolution matched to the smallest feature to detect (e.g. 50 MP to resolve sub-5pt characters), frame rate matched to the line speed — nothing over-specified, nothing under-resourced.
Six-sided coverage, including from below
Corner-mounted arrays, glass-panel bottom cameras, and compact open rigs for natural-flow imaging — coverage designed around the object geometry, not around what is easy to mount.
From frame grabber to the factory floor
Frame grabbers, edge compute, and MES/ERP output so inspection results land where operations already work — not isolated on a standalone PC.
How we work
From part to proven rig — four steps
01
Define what varies.
Object, and what changes across it: size, orientation, surface finish, working distance, ambient light. A thorough variation map prevents surprises during commissioning.
02
Illumination first.
We select lighting before optics. Most teams do it backward and fight glare and inconsistent contrast for the life of the system.
03
Match sensor, optics, and mount to the spec.
Camera, lens, and mechanical mount chosen together, not in isolation — because a perfect lens specification fails if the mount introduces vibration.
04
Deliver a rig proven on your parts.
Hardware-ready, validated on the client's actual samples, in an ~8-week turnaround (4–6 weeks procurement + 2–3 weeks design and integration).
Proof in practice
Heat, scale, precision, throughput
The object changes, the discipline doesn't. Get the image right and the software follows.
Imaging glowing steel at 600–900°C at full line speed
Hot steel strip inspection
A fully integrated flat carbon steel mill needed inline defect detection on hot-rolled strip running continuously through a Compact Strip Mill. An AI and machine vision system was deployed to detect and classify surface defects in real time, covering 100% of strip area at production speed despite extreme temperature.
600–900°CStrip temperature handled
Read case studyImaging a ~60 m object — mobile cart-based acquisition rig
Wind turbine blade acquisition
Wind turbine blades run approximately 60 metres long. Manual inspection took 90 minutes per blade (both halves). An automated mobile inspection system built around a moving cart enabled full-length image acquisition and detection of air bubbles, delamination, cutting cracks, dents, and step wrinkles.
~60 mBlade length imaged
Read case studyMicron precision — dual line-scan cameras on a shared axis
Nuclear fuel pellet measurement
India's largest nuclear research centre required automated dimensional measurement of zirconium fuel pellets. A vision system with dual line-scan cameras fixed on the same axis delivered the micron-level precision needed for nuclear-grade fuel production.
MicronMeasurement precision class
Read case studyPer-grain analysis on 100 g–1 kg samples at industrial throughput
AI grain analyzer
Rice mills needed objective, repeatable quality grading across 100 g to 1 kg samples per run — replacing subjective manual inspection. An AI-driven platform combined real-time grain detection, dual-model segmentation, and per-grain measurement of length, width, chalkiness, and whiteness at industrial throughput.
100g–1kgSample size per run
Read case study15+
Years
100+
Imaging Solutions
25+
Global Hardware Partners
~8 Weeks
Hardware Turnaround
Getting inconsistent detections? It's probably the image, not the algorithm.
Send us your part and the detection goal — we'll scope the imaging setup that makes it reliable.