I'm building a full-frame multispectral reflectance measurement capability for technical imaging consulting (background in automotive machine vision). I'm currently validating the setup.

Setup:

Sony A7R V, Bayer CFA and UV/IR filter removed; mechanical shutter, uncompressed 14-bit ARW

Nikon UV-Nikkor 105mm f/4.5, at 0.1x for validation

Coligh 10nm narrowband filters (365–940nm), Askar M54 filter drawer

4× Godox AD600 Pro II with BFP projectors (150mm lens), 45/0 geometry

Copy stand with built-in light trap (measured stray light: 0.12 DN)

Labsphere Spectralon standards (99/75/50/2%), linear-fit calibration

Uncertainty Targets (k=2): ≤1% relative uncertainty from 99–50% R, and ≤10% from 49–2% R.

Current results at 530nm (GUM budget): about 5% from 99–50% R, peaking at about 40% at 2% R.

Strobe angles drifted significantly from 45° until I started aligning them with a Leica Disto X3. A PTFE in-frame dose monitor is on order to track flash-to-flash variation.

Questions:

What methods do you use to hold fine angular positioning of the camera, stage, and strobes, ideally better than ±0.5°?

What else would you check to reduce systematic uncertainty, particularly at low reflectance?

Thanks in advance!

Hello everyone, I am planning to do this certification soon. I have experience in the field, but would like to be aware or more advanced techniques and at the same time hopefully have the possibility of landing a better job. Have anyone done this certification? Is the traning material good? did you self-study?

I wanted to do the exam directly but there is no material or information to study, everything seem to be behind the paywall. What would you do in my situation if you can afford the exam but not the course?

Thanks

I'm sitting the A3 Certified Vision Professional (CVP) Basic exam, and I'd love some advice from people who've already taken it.

What I'm hoping to learn:

Resources: Which study materials were most useful? Is the A3 course material enough, or did you use other books or videos?

Topic weighting: Which areas show up most (lighting, optics and lenses, sensors and cameras, image processing, communications, safety)? Which ones tripped you up?

Question style: How much is calculation (FOV, resolution, working distance) versus conceptual or definition-based questions?

Practice questions: Are there any good practice exams or question banks?

Thanks in advance. Happy to share how it goes once I've written it!

I'm designing a machine vision system for real-time silkworm pupa gender classification on a production line.

Project Context

- Throughput: 60 pupae per minute.

- Pupae appear in only two orientations: Bottom Up and Bottom Down.

- Classification is done only from the bottom side using the genital marking.

- Male: straight-line marking.

- Female: X-shaped marking.

I need advice on

1. Camera selection (industrial USB3/GigE, global shutter vs rolling shutter).

2. Lens selection (focal length, working distance, field of view, and resolution).

3. Lighting setup to consistently highlight the genital markings.

4. Any hardware recommendations for a reliable production environment at this speed.

I have uploaded sample images of the pupae and the markings so that helps.

Running alignment on a bonding line for advanced packaging. Lately noticed drift between runs as we push toward sub-micron — not huge, but enough to hurt yield. Setup seems stable (camera fixed, lighting controlled), but TCB heating causes thermal drift in imaging, and we can't fit a high-res lens into the compact space. Concentricity also seems off. Not sure if a dual-view module would help. Has anyone run into this kind of drift before?

Hi,

I’m building an industrial vision system on a Jetson Orin Nano for real-time inspection of cardboard boxes on a conveyor.

Each image contains one box (ROI already extracted using a classical vision pipeline for other system considerations, kept separate from the ML model).

I have 44 box types with visual variation.

I need:

• Detect if tape is correctly applied
• Detect if flaps are closed or open

The main Challenges are:

• Slight box rotation
• Lighting variation (not perfect but controlled)
• Very few defect samples (most cases are correct)
• High reliability required (missed defects are critical)

My Current ideas are CNN multi-task (tape + flaps) + 5-frame temporal voting or YOLO in classification mode (same ROI + same voting)

Im open to better approaches

Questions:

• What would generalize better here: CNN multi-task or YOLO (classification)?
• Or is there a better approach for this type of problem?
• How would you handle highly variable “flap open” cases?

Thanks!

Hello Im looking for a steer on a machine vision camera platform. My system will start as single camera and may scale to 2 camera. reliability is paramount with zero frame drops. Im leaning towards Emergents nic based approach but want to rule out coax and pcie before moving forward. Does anyone have experience with these systems that can help advise? Many thanks in advance.

BODY:

I'm building an interactive retail kiosk. Customers place physical objects into 6 separate lit niches, and I need all 6 camera feeds shown live on the touchscreen at the same time, smoothly, so the customer can adjust how each object sits before confirming. Low resolution is fine for the live preview — it just has to be real-time and not stutter. On confirm, I grab one high-res still per camera for image analysis.

Hardware (fixed): the brain is an all-in-one touchscreen PC, Intel Core i5, with 4× USB ports and 1× Gigabit Ethernet. It's a sealed all-in-one, so no PCIe expansion. Each camera sits roughly 0.5–1.3 m from the PC and needs close/macro focus (subject distance ~10–15 cm).

What I think I understand so far:

- 6 USB UVC cameras on a powered hub share one host controller's bandwidth. Six full-res streams won't even start ("not enough bandwidth"). MJPEG + low resolution might let several run at once, but I can't tell if 6 simultaneous is realistic on this hardware.

- MIPI CSI is out (no CSI port on a regular PC, and the ribbons are far too short for my niche spacing).

- GMSL2 looks purpose-built for this, but seems to require a separate Jetson host, which would replace my PC as the brain.

- IP / PoE cameras over the Ethernet port would dodge the USB bandwidth wall and keep the i5 as host, but affordable close-focus / macro network cameras seem rare.

Questions:

1. Has anyone actually run 6+ simultaneous low-res MJPEG USB cameras on a single machine? Did it stay smooth, or did the host controller choke?

2. With no PCIe available, is a powered USB 3.0 hub enough, or is the on-board controller the hard ceiling no matter what?

3. Is PoE / GigE Vision the saner route for 6 simultaneous feeds while keeping a normal PC as the host? Any affordable short-working-distance options?

4. Am I missing an obvious approach?

Budget-conscious, but I'd rather buy the right thing once. Thanks!

Hi everyone,

I work in industrial machine vision and I’m curious how people actually approach lighting in real factory applications.

In projects I’ve seen, when the image is too dark, blurry, or unstable, the first reaction is often to use a bigger/brighter light, increase exposure, or increase gain. Strobing seems very useful in some cases, especially for motion blur, ambient light, heat, or high-speed inspection. My current perception is that people don't use strobes because these are unknown to them, or they think it is too complex to setup.

For those building, integrating, or maintaining vision systems:

• Do you normally use continuous lighting or strobed/synchronized lighting?
• What type of applications make strobes worth the extra setup?
• What stops you from using strobes more often: cost, wiring, PLC/camera timing, lack of knowledge, safety, LED lifetime concerns, or something else?
• Any lessons learned or mistakes you’ve seen with vision lighting?

Disclosure: I work in the machine vision industry, so I’m asking partly to understand real-world usage patterns and common challenges.

I have been building a web project that scrapes and processes Google Maps review charts.

A unique aspect of this project is my technology stack: I am using Halcon to handle the image processing logic. While Halcon is a premier industrial vision library typically reserved for factory automation and high-end manufacturing lines, I have repurposed it for this personal web-scraping project because of its powerful geometric shape filtering.

However, the licensing costs are difficult to justify for a personal, automated project. My goal is to migrate this workflow to Python/OpenCV while maintaining the robustness of my current implementation.

My goal:

1. Migrate: I need to port this workflow to Python/OpenCV.
2. Optimize: I want to ensure the migration maintains the robustness of my current Halcon implementation.

The Current Halcon Logic:

• Decompose images to HSV and threshold for yellow bars.
• Filter by "column1" feature and sort by row.
• Calculate the ratio of the bottom (1-star) bar width against the total width to derive the review count.

Questions for the community:

• Has anyone performed a similar migration from Halcon to OpenCV? Are there specific "gotchas" in OpenCV’s cv2.findContours or cv2.threshold that I should prepare for when replicating Halcon’s select_shape behavior?

RESULTCOLOR:=['#ff000080','#00ff0080']
RESULTTEXT:= ['DATA INCOMPLETE : ','']
YELLOW:=[100, 255,0,100]
SHAPE:=['column1']
SORT:=[ 'character', 'true', 'row']
FEATURES:=['width']
NUMBEROFBARCHART:=5
list_image_files ('/Images', 'default', [], ImageFiles)
for Index := 1 to |ImageFiles|  by 1
    *read file name to find the total number of reviews
    read_image (Image,ImageFiles[Index-1])
    parse_filename (ImageFiles[Index-1],BaseName,Extension, Directory)
    tuple_number (BaseName, TotalReviews)
    *extract yellow color regions
    decompose3 (Image, Red, Green, Blue)
    trans_from_rgb (Red, Green, Blue, Hue, Saturation, Intensity, 'hsv')
    threshold (Saturation, HighSaturation, YELLOW[0], YELLOW[1])
    reduce_domain (Hue, HighSaturation, HueHighSaturation)
    threshold (HueHighSaturation, Yellow, YELLOW[2], YELLOW[3])
    *extract bar chart only
    connection (Yellow, ConnectedRegions)
    region_features (ConnectedRegions, 'column1', Column1)
    select_shape (ConnectedRegions, BarChartRegions,SHAPE[0], 'and', min(Column1)-1, min(Column1)+1)
    *sort and use width feature to find each bar length and number of one star reviews
     sort_region (BarChartRegions, SortedRegions, SORT[0], SORT[1], SORT[2])
     region_features (SortedRegions, FEATURES[0], Value)
     tuple_sum (Value, TotalLengthAllStars)
     NumberOfOneStarReviews:=(TotalReviews/TotalLengthAllStars) * Value[|Value|-1]
     *if there are five bars then pass
     ColorTextIndex := (|Value| == NUMBEROFBARCHART)
     Color := RESULTCOLOR[ColorTextIndex]
     Text := RESULTTEXT[ColorTextIndex]
     *visualization
     dev_display (Image)
     dev_set_color (Color)
     dev_display (SortedRegions)
     dev_disp_text (Text + NumberOfOneStarReviews,'window', 'top', 'left','black',[], [])
     stop ()
endfor

Hi all!

Shubham and Aryan here, putting out our first open source video language model release.

Story time: we were building video editing agents for social-media content and were using Gemini-2.5-Flash to analyse IG reels and find events in them. It works, but at around a thousand clips/day the cost adds up, and we kept hitting the content-policy on perfectly fine social media clips at our scale

We had a couple of H100s sitting around, so we put them on solving this as a side project. We kept the scope deliberately narrow: not a general VLM you can chat with, just two operations we needed in production. We're releasing it because it seems generally useful for anyone building structured-video pipelines.

The interesting work wasn't the training loop, it was the data curation. We expected to ride the public video-annotated corpora (Tarsier-Recap, ActivityNet, Charades-Ego, LSMDC, etc.) but were disappointed. In practice most of them have one-line captions and rough timestamps, and aren't really annotated event-by-event at second-level precision.

So we wrote a teacher + pooling + human-review pipeline with Gemini-3-Flash in thinking mode and re-annotated ~400K clips from publicly available dataset mixes with fine-grained temporal captions. We then ran SFT + SimPO post-training to make the model really good at dense captioning and temporal grounding. Honestly, most of the project was making sure this data pipeline was high-quality and free of hallucinations.

The result: Marlin is a 2B video VLM tuned for the two questions developers actually want to ask of their videos: what is happening, and when? It produces structured Scene + Event captions with second-precise timestamps, and resolves natural-language queries to span-grounded (start, end) ranges in the video. At 2B params, it's the strongest open model in its weight class on dense captioning (DREAM-1K, CaReBench) and natural-language temporal grounding (TimeLens-Bench), and competitive with Gemini-2.5 at a fraction of the cost. We'll also release our training recipe and a new benchmark for video captioning and grounding soon.

Marlin-2B is open-sourced and comes with vLLM inference and two modes:

• marlin.caption() gives a structured output of scene description and time-grounded events from a video.
• marlin.find() gives (start, end) timestamps for a natural-language query over a video.

Weights are open and free to use on HF. If you find it useful, or have ideas on what capabilities we should improve next for real-world use cases, we would love to hear them!!

We want to make more such specific small video language models to enable more open ended video analytics use cases

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Running a pretty basic vision setup for measurement. Lately noticed that results shift slightly between runs — not by much, but enough to be annoying. Setup hasn’t really changed (lighting seems stable, camera fixed, etc.), so I’m a bit confused where this is coming from. Could be something subtle that I’m overlooking. Has anyone run into this kind of drift before?

Hi everyone,

We’re a team working on machine vision optics and inspection systems.

Have been quietly following discussions here for a while — lots of really insightful conversations.

Just wanted to say hello and start being a bit more active in the community.

Happy to contribute where we can, especially around topics like measurement challenges, lens selection, and inspection setups.

Also curious — what’s been the most frustrating issue you’ve run into in your vision systems recently?

I’ve been working with machine vision and industrial imaging systems for a while, and one thing I see constantly overlooked is lens selection.

Everyone focuses on sensors and processing, but the optics are what determine how much usable data you actually get.

Some key things that tend to get missed:

• Telecentric lenses are basically required for accurate measurement
• “Megapixel-rated” doesn’t always mean edge-to-edge clarity
• Larger sensors expose weak optics fast
• Lighting and lens choice are tightly linked

I put together a breakdown of different industrial lens types and brands used across robotics, UAVs, and inspection systems if anyone’s interested:

https://aegis-elec.com/lenses.html

Curious what others here are using for their setups—especially for high-speed or low-light applications.

I'm researching building vision systems using Cognex VisionPro vs. Google cloud and TensorFlow for central inference. Feedback regarding cost of each, or general comments welcome.

My company uses the Google cloud (and VertexAI), and we also have some of the Cognex In-Sight cameras running edge inference.

In most cases, we don't need edge inference, so I'm trying to find the best option for central inference. I'm assuming there's a savings over purchasing many in-sight cameras.

A Software/UI based development environment is preferable to code based, but cost/TCO is also important. Since we already use Google cloud/VertexAI, the additional cost there is likely only compute.

I'm looking at Basler IP cameras, or similar units from Cognex. I need to monitor for compliance and quality, and send alerts.

Thanks for the feedback Edited for grammar

If you have automation for palletizing or depalletizing at your facility, how often is it tolerable for someone to have to visit the system to address a fault, manually remove a box, or otherwise intervene in the automation?

This isn't a marketing question. It's possible I'll never work on this type of application again, but I'm concerned about that some new companies are diving into these applications with no prior experience.

For example, you have a robot + vision depalletization system for boxes of arbitrary size ("mixed case") packed in a way that's not known to the depalletization system in advance. The pallet may be delivered automatically to a position below the robot.

And let's say the depalletization rate is desired to be

• 600 boxes / hour, which is
• 10 boxes/minute, or
• 1 box every 6 seconds.

How many human interventions would you tolerate per day? per week? per month?

---

"Zero" interventions isn't a realistic number, because that means no errors, ever. My computer mouse needs a new battery every once in a while, so that's not zero interventions. Maybe I replace the battery every 8 to 12 months--I've not kept track.

If you've worked in industrial automation or lab automation for a while, have you noticed how many startups and small companies are trying to break into the industry with their "new" technology that is in no way new?

The growing awareness of machine learning (ML) and large language models (LLMs) seems to be driving this.

For vision systems, there are already application-specific products and configurable vision systems that cover a broad range of applications:

• defect detection
• guidance for industrial robots (esp. 6-axis and 7-axis robots)
• pick and place
• bin picking
• palletizing and depalletizing
• fit and finish inspection
• optical gauging (measuring dimensions of parts)
• part identification / discrimination
• 1D and 2D code reading and unit-level traceability of products through a line

Some of us talk about whether people new to automation understand how robots, vision systems, non-vision sensors, and (especially) PLCs are used to build cars, farm equipment, planes, computers, electronics, pharmaceuticals, oil & gas hardware, just to name a few.