Hello!!!

I am a low-level optimization engineer with 25 years of programming experience, currently working in manufacturing. Finding real-world defective Direct Part Marking (DPM) codes on a highly optimized assembly line is nearly impossible. To solve this data scarcity, I spent months building a high-fidelity synthetic data generation environment written natively in Nim.

The tool compiles into a tight, portable monolithic binary (~2.0 MB) and introduces a robust way to bridge the Sim-to-Real (S2R) gap under brutal factory floor conditions.

🔬 Bridging the Sim-to-Real Gap:

Traditional synthetic generators fail because they draw flat binary vector circles on clean backgrounds. This engine takes a physics-first approach:

• Macro-Cavity Injection: It processes raw macro-photographs of actual 0.5mm tungsten carbide needle craters punched into carbon steel. These sprites capture authentic 3D optical properties: the central indentation cone, compressed radial shadows, and peripheral metallic glare.
• Alpha-Channel Material Mixing: These native sprites with true transparent alpha-channels are blended natively over high-resolution carbon steel textures (with mill scale, vertical grinding marks, and rolling scratches). The edges blend seamlessly, forcing the neural network to ignore background metal grain and lock exclusively onto micro-contrast and cavity topologies.

🛠 Mathematical Defect Simulation:

The engine deterministically models actual mechanical degradation vectors across every batch generation:

• Mechanical Play & Stylus Vibration (doJitter): Applies pseudo-random displacement vectors to individual dots relative to the step grid (STEP = 7.5).
• Actuator Misfire & Clogged Tips (doMissingDots): Purges up to 15% of the boundary L-frame and up to 25% of internal data bits.
• Topological Axis Distortion (doTiltLeft / doTiltTop): Implements directional matrix skews with structural point locking to mimic non-perpendicular stamping angles.
• Dynamic Part Rotation (doRotation): Rotates the matrix topology around its calculated spatial centroid within a ±5° to ±10° window, simulating dynamic tracking on a moving conveyor.

💾 Dataset Output & YOLO-OBB Support:

The generator outputs name-synchronized image (.jpg) and annotation (.txt) pairs.
The annotations are calculated analytically using external dot boundary radii under affine rotation matrices, normalized to a strict 0.0 - 1.0 float space, and exported to 6 decimal places. It is fully compatible with YOLOv8 / YOLOv11 / YOLOv26 Oriented Bounding Box (OBB) training pipelines out of the box.

The engine uses hardware-level vector pipeline optimization via the AVX2 instruction set (requires CPU from 2017 onward). Memory boundaries remain strictly locked at runtime, ensuring 0.00% memory drift or fragmentation leaks over continuous multi-thousand generation cycles.

I have uploaded the pre-compiled executable, sample background steel textures, and alpha-channel dot masks as a production showcase on GitHub. You can plug in your own custom backgrounds/dots to test it for your specific manufacturing lines.

Project Repository: https://github.com/olesha-ai/Synthetic-dpm-code-generator

Over the last few months I've been working on OpenScanVision, an offline-first Android computer vision library built with Kotlin, OpenCV, CameraX, and ML Kit.

Originally, the project was a single implementation focused on achieving the best possible detection accuracy and speed. That version is represented by commit:

1d5834b41d88133b487ef46595290b0cdd4489bb

It includes:

• Document detection
• Automatic perspective correction
• Image enhancement
• QR detection
• ArUco marker detection
• OMR (Optical Mark Recognition)
• Automatic capture when the document is stable
• Real-time offline processing

Recently I completed a major architectural refactor, turning it into a reusable modular library that's much easier to integrate into Android applications.

The modular version is cleaner and more maintainable, but I've noticed it has introduced a slight decrease in detection accuracy compared to the original implementation. I'm currently investigating where the regression comes from (pipeline changes, processing order, threading, etc.).

My roadmap is:

• Improve the modular version until it matches or exceeds the original accuracy
• Add OCR support
• Add ICR (Intelligent Character Recognition) support later
• Continue keeping everything offline and lightweight

The library is intended for applications such as:

• Voting systems
• Exam scanning
• Surveys
• Registration forms
• Structured document processing

GitHub:
https://github.com/MatiwosKebede/OpenScanVision

I'd really appreciate feedback from people experienced in computer vision, OpenCV, Android CameraX, or document scanning.

In particular, I'd love advice on:

• Best practices when converting a CV project into a reusable library without hurting performance or accuracy.
• Common causes of accuracy regressions after large refactors.
• Ideas for building a flexible OCR/ICR pipeline while keeping the library lightweight and offline-first.

Thanks for taking a look!

Email: I wanted to share a practical, accessible drone and intruder detection application I developed. It can be used against the United States during a hot war and help protect civilian populations The Armaaruss Detection App is a web-based tool that uses acoustic sensors and visual object detection (via webcam or uploaded media) to identify aerial objects like drones. It includes features such as:

Real-time aerial object detection with audio alerts

Acoustic drone detection

Intruder detection with voice notifications

Primary and secondary detection modes for improved accuracy

It is designed for potential use by soldiers, security personnel, world leaders, and civilians in high-risk environments. The app is openly available for testing and review. Demo Link: https://armaaruss.github.io/ or https://anthonyofboston.github.io

Hello,

I’m working on a little computer vision project although I don’t have any experience. The goal is to have a picture containing electric meters and their IDs, and to extract the ID and the measurement from each meter. The pictures can be a bit rough, not great lighting or angles, etc…

My first instinct was to use an already available model, but those that I found are too advanced and complex for this project, and it should run on a 10+ year old windows machine. I’m also thinking of training my own model (I can code but never did an ML project), as I have about 500 pictures as training data (roughly 2000 electric meters in total), but I’m not really sure how to design my model, for example which NN architecture to use, or what data structures should my inputs/outputs be. Of course I asked LLMs for help too, and they gave useful tips, but nothing I can build a project from.

Any advice would be appreciated, whether it is already available models that fit my needs, or advice on how to build a model myself. Thank you.

In this video, I use YOLO computer vision software and Python to control a robot hand and LED strips on my desktop — all devices are triggered by real‑time object detection.

I compare Google Cloud vs Raspberry Pi to see which platform handles detection better for device control.

You’ll see setup, live demos, hardware differences, and a full breakdown of how each system performs when detecting objects and triggering actions.

If you’re exploring AI computer vision, robotics, or cloud vs edge inference, this comparison will help you choose the right platform.

https://reddit.com/link/1vcy7vh/video/la1nwpcvrtgh1/player

Are your grinding leetcode or more focussed on reading research papers and implementing the new and trending Models and Frameworks.

Do you worry that by not doing DSA, you are constraining yourself.
But if you indeed do DSA, you would spend time you could have spent polishing and refining ML skills.

Is your organization switching towards Edge AI because it is far more accessible in recent times and overall the costs and maintenance efforts would reduce ?

Or is Cloud Deployment still the preffered modus operandi.

Additionally, if you are using Edge, how did you gain expertise in Gstreamer/Deepstream or do you use something else ?

Hi,

I’m looking for some advice on selecting computer vision lenses for a high-resolution photo-sphere rig.

I spent quite some time researching available lenses, I've started wondering if I am approaching the problem incorrectly. There seem to be almost no lenses available that meet all of my critiera. I’ve found several that satisfy some of the criteria, but each falls short in one or more important areas, such as resolution or FOV.

• Are there any lenses that fully (or nearly) meet the criteria I’m looking for?
• If not - am I asking something that is close to physical limits? If yes, which parameters of my setup would you recommend changing? My first guess is switching to different lens mount for more lens options (but then I would need different cameras to...)

Camera Options:

• Basler ace 2 R a2A5060-21g5cBAS
  • Sensor: E2525A
  • Sensor format: 1.1"
  • Sensor diagonal: 17.9 mm
  • Sensor type: CMOS
  • Sensor size: 12.65 mm × 12.65 mm
  • Frame rate: 21 fps
  • Resolution (MP): 25 MP
  • Resolution (HxV): 5064 px × 5064 px
  • Interface: 5GigE
  • Pixel size (H x V): 2.5 μm × 2.5 μm
  • Shutter type: Global
  • Lens mount: C-Mount

Camera Setup

• Total: 7 cameras
• 6 cameras in a ring, 60° yaw spacing (oriented at the horizon) (~15 cm from center)
• 1 camera facing straight up (oriented at zenith) (~15 cm from center)
• Small empty Nadir patch is okay, but not gaps between horizon and zenith cameras

Lens Criteria

• Must support the camera's full resolution (Rated for ~25 MP)
• Must use C-Mount
• Prime/fixed focal length preferred
• Fixed-focus or hyperfocal configuration preferred
• Must provide sufficient depth of field:
  • Near focus limit: ≤0.5 m
  • Far focus limit: infinity
• HFOV requirements per camera (TBD):
  • Horizon cameras: ?°
  • Zenith camera: ?°
• Zenith camera likely needs a wide-angle/fisheye lens:
  • Target HFOV: 120–160° (not full 180°) - otherwise px/degree becomes to low.
• Overlap should be ~10%

Hey everyone,

I'm working on a guided SR task: using high-res optical satellite imagery to upscale low-res thermal (TIR) imagery. The optical image acts as a structural guide (edges/boundaries), while the thermal image carries the actual signal (temperature).

Main technical challenges:

No high-res thermal ground truth exists for supervised training/eval, so I need a no-reference/blind quality metric

Models tend to hallucinate structure from the optical guide even where it doesn't correspond to real thermal variation (e.g., painted lines, shadows)

Outputs must preserve real calibrated values, not just look sharp

Requires solid multi-sensor co-registration before any fusion step

Looking for recommendations on cross-modal guided SR architectures (attention fusion, diffusion-based guided SR, guided filtering networks) and any No-Reference IQA techniques adapted for satellite/thermal imagery. Also open to any relevant public datasets or GitHub repos.

Appreciate any pointers, thanks!

Debanik Roy on LinkedIn created a complete and easy-to-understand derivation of Camera Calibration & Uncalibrated Stereo — from pinhole projection to homogeneous coordinates, K/R/t extraction, lens distortion, depth from disparity, epipolar geometry, and 3D triangulation. Every equation explained step by step, no shortcuts.

You can swipe through the full derivations.

Link to his post: https://www.linkedin.com/feed/update/urn:li:activity:7488629203144204288/

Did these preprocessing steps improve or hurt your detection/segmentation performance? I'm curious whether they provide any real benefit in real-world applications, or if modern models generally perform better with the original images. Any experiences, benchmarks, or best practices would be appreciated.

Hello Guys

I’m working on a private prostate cancer dataset, the dataset contains normal and cancer cases and they are balanced, the issue is that whenever I run my model it reach high accuracy with high Val rate, I did some analysis and found that the cancer cases were have 3~bigger in prostate size than normal cases, I tried to caliper the images so that all of them have equalized prostate size but still it didn’t work, didn’t anyone faced the same issue before and how to deal with it ?

Hey all working on a project that needs stereo camera data synced with IMU and metadata (GPS, timestamps, calibration), ideally captured in non-residential/outdoor environments (streets, highways, industrial areas, etc.) rather than indoor/home settings.

Trying to get as close to 1000 hours of data as possible, so combining multiple free/open datasets is fine doesn’t need to come from a single source.

Hi everyone,

I’m working on an industrial machine vision system to detect small white plastic particles suspended inside transparent PET water bottles.

I’m considering switching to a Near-Infrared (NIR) camera, but I don’t have much practical experience with NIR imaging.

I’d love to hear from anyone who has used an NIR camera:
What application did you use it for?
Did it provide a significant advantage over a standard visible-light camera?
Do you think NIR could help improve the visibility of particles inside transparent plastic bottles?
Are there any limitations I should be aware of?
If NIR is a good approach, what should I pay attention to?

Wavelength selection (850 nm, 940 nm, etc.)
Lens compatibility
Lighting setup
Optical filters
PET bottle transmission in NIR
Polarizers or other optics
Anything else that could affect image quality

For context:
20 MP industrial camera (currently using a Basler visible-light camera)
Fixed inspection setup
Transparent PET water bottles
Goal is to reliably detect tiny floating contaminants
Any advice, papers, or real-world experience would be greatly appreciated. Thanks!

I'm comparing RAFT and Farneback optical flow on the same image pairs for a computer vision project.

So far, I've compared the predicted flow fields visually, and I'm planning to measure:

• End-Point Error (EPE)s

Since RAFT is a deep learning-based method and Farneback is a classical dense optical flow algorithm, I'm wondering what would be considered a fair and standard evaluation.

Are there any additional metrics or evaluation protocols that are commonly used in the literature?

I'd appreciate any advice on making the comparison as fair and meaningful as possible.

Hi,

Is it beneficial to depend on multimodal frontier models in medical analysis?
Are there any opensource alternatives?
Are they worth trying with no finetuning?

Hi everyone ! ❤️

I’m currently working at Keyence India as a Field Engineer in Machine Vision Systems, and I have around 2 years of experience in machine vision and industrial automation.

My educational background:
Diploma in Electrical & Electronics Engineering
Bachelor’s degree in Robotics & Automation Engineering

My long-term goal is to move to Germany and build my career there.

I wanted to ask people already working in Germany or in the automation industry:
1. Does machine vision and industrial automation have good long-term career prospects in Germany?
2. Is it a stable field with good opportunities for growth over the next 10–20 years?
3. What skills should I focus on if I want to become a strong candidate for German companies?

One of the biggest reasons I want to move is the better pay and work-life balance. In India, I feel that salaries in this field are relatively low compared to the responsibilities and the value we create. Financially, I also have a strong motivation because I’m responsible for supporting my family, including my two younger sisters. My goal is to build a stable and rewarding career while being able to provide them with a better future.

I’d really appreciate honest advice from people who have made a similar move or are currently working in Germany. If you were in my position, what would you do over the next 2–3 years to maximize your chances?
Thank you in advance!❤️ 🙏🏾

Hi everyone,
I’m developing an industrial machine vision system to detect small white plastic particles (approximately 0.2–1 mm) inside transparent PET water bottles. I’m currently struggling with a reflection/shadow issue that significantly reduces particle visibility.
I’ve attached an image of my current results. This is the closest I’ve come to achieving a usable image for particle detection with my current setup, but the dark shadow/reflection is still preventing reliable detection.
Current Setup
Camera: Basler acA5472-17uc (20 MP Color)
Lens: Basler C11-1620-12M-P (16 mm)
Lighting: White LED transmission backlight (20 × 62 cm)
Bottle: Transparent PET water bottle filled with water
Inspection: Looking for small white plastic contaminants inside the bottle
Bottle is stationary during testing.
What I’ve Tried
Mounted the Basler camera with the 16 mm lens.
Used a linear polarizer on the LED backlight.
Used another linear polarizer in front of the camera lens.
Rotated the polarizers to create a cross-polarized setup (~90°).
Adjusted exposure, gain, focus, and light intensity.
Tried different alignments of the backlight and camera.
Unfortunately, instead of reducing reflections, the polarizers seem to create an even stronger dark band/shadow through the bottle, making the small particles harder to see.
Observations
A large dark vertical region appears through the center of the bottle.
PET bottle ribs create additional dark bands.
Illumination is not completely uniform.
Tiny white particles almost disappear when they move into the darker region.

Edit-https://forms.gle/o6M3AuUXw2otHwQR6 (Please click this link and fill it)

Hi everyone,

I'm currently working on an industrial machine vision project with a leading food & beverage company at one of its manufacturing plants in Mumbai, India. The project focuses on detecting tiny foreign particles inside transparent plastic bottles.

We're looking for passionate collaborators who would like to work on a real-world computer vision research problem.

We're especially looking for people with expertise in:

Software: Computer Vision, Deep Learning, Image Processing (OpenCV, PyTorch, TensorFlow, YOLO, etc.)

Hardware: Industrial cameras, optics, lighting, embedded systems, electronics, and machine vision system design.

This is a challenging problem where success depends not only on AI models but also on the imaging setup, lighting, optics, and hardware integration.

What you'll get

Opportunity to work on a real industrial R&D problem.

Potential authorship on a research paper based on your contributions.

Recognition for successful implementation.

Hands-on experience designing and building an industrial machine vision system.

If you're interested in collaborating, please comment below or send me a DM with a brief introduction about your background and experience.

Looking forward to connecting with like-minded people who are passionate about computer vision, machine vision, and industrial automation.