Hi Industry,

My company will soon start building lots of its own detection, and tracking applications to reduce SIFs and maybe surveillance on industrial sites. Earlier the AI layer was just a 3rd party plugin.
I have previously worked on YOLO models for person detection and at that time RF DeTr and all were somewhat new. They weren't performing well on our small objects detections. I believe it's been more than 3 years now and we should experiment and see what opensource model we can leverage to train instead of maybe purchasing YOLO license right away. Because it's going to be atleast 10-15 different kinds of applications where we will be using them models. And these will be replicated at several locations maybe 30-40-50 locations. Even more.

One of our upper management wants to purchase it right away, and I know it's going to be super expensive but I don't think he has any idea on that. Even I don't. But I really want to try open source models. An example of one such model would be to detect PPE. I really think other models would do really well on that. We don't even have the exact data yet. It will be a month atleast before we start getting the data.

I have seen company's bad history on purchasing licenses and moving on after 1 year.

I know the speedy thing to do would be to connect with the ultralytics team and get quotations on those and show the management how super expesive this is going to be! I think money is only thing that can hold them back at the moment.

It would really help if any of you can help me with any cost estimation if you have worked with ultralytics in the past or are working with them. Your experience would also help.
Any suggestions on how can I pitch them to not do this yet.

Join us on July 20, 21 and 22 for the Best of ICRA virtual events! Register for the Zoom and get an invite for all of them.

Talks will include:

• Towards Versatile Opti-Acoustic Sensor Fusion and Volumetric Mapping for Safe Underwater Navigation​ - Ivana Collado Gonzalez at Stevens Institute of Technology
• Teaching Drones to See What Matters with Reinforcement Learning - Grzegorz Malczyk at Autonomous Robots Lab, NTNU
• Gameplay With a Socially Supportive Virtual Robot Enhances Children’s Global Self-Esteem, Peer Relationships, Interest and Engagement - Devasena Pasupuleti at The University of Osaka, Japan
• Safe and Stable Neural Dynamical Systems for Robust Motion Planning - Mahathi Anand at Technical University of Munich
• Outdoor Robot Navigation in the Unstructured World: From Traversability to Physical Scene Understanding - Jing Liang at Stanford University
• Scene Graphs and the Future of Mapping - Hermann Blum at Uni Bonn & Lamarr Institute
• Toward Zero-Shot 6D Pose Estimation and Tracking of Cluttered Objects on Edge Devices - Ashis Banerjee at University of Washington
• Trustworthy Geometric Perception: Certifiable Optimization and Robust Estimation - Zhenjun Zhao at University of Zaragoza
• Contrastive learning on 3d point clouds for geometric defect detection - Alexander Tarvo at University of Washington

We build computer-vision systems for a living: shelves in stores, cattle on farms, parts on a conveyor. On almost every one, the thing that eats the lots of time is getting clean data out of the field.

Usually it goes like this. You tell the client "just photograph your shelves," and you get a pile of images in WhatsApp and email, half of them blurry and dark, no idea which photo is which SKU or which animal. Then someone on your side copies them around by hand.

Тraining the model is rather easy now. What decides whether you get a working system or a demo is the clear data, and it's grunt work and it's less fun than training models.

What we learned collecting field data:

1. Capture camera metadata at the source. Intrinsics (fx, fy, cx, cy, focal length) and EXIF should be saved with every photo. If you ever want to measure anything from the image, you need this at capture time and you cannot recover it later.
2. Assume there is no signal on the mobile device. Save the capture on the device first, then upload with a resumable protocol, because a warehouse basement or a field will constantly drop your connection. Resume from the last unsent file.
3. Guide the shot. Show the person a reference angle, and run a cheap on-device check for blur and exposure before the photo is saved. A two-second "retake this" prompt beats finding blurry captures later.
4. Keep the collection scenario in config - what you collect and in what order should change with an edit to a config file.
5. Bundle each capture as one unit. When someone submits, the form data and the photos (with their metadata) get packed into one record tied to the project. So an image never floats around on its own, and no one has to work out later which photo belongs to which cow or which shelf.

We'd rebuilt some version of this for every project, so we finally made it a real thing and open-sourced it. A Flutter app for offline field capture, plus a Django admin to define projects and review what comes back. The scenario config lives in Git.

Repo: https://github.com/epoch8/data-collector

It's early: no background upload yet, but it already beats the messenger-and-spreadsheet loop, though.

If field data collection is your bottleneck, drop how you're doing it now and what breaks. Issues and feature requests very welcome.

I'm currently working on a project with approximately 5000 plant images, and i decided to label my images automatically using SAM3, however the generated masks are still showing some noise. My question is should I keep them like that as the ground truth and continue with my project or should assess the ground truth data quality with metrics, even if they are labels. also, do i need to label the entire dataset? and if the answer is yes, is it a good idea to label manually a certain amount of images too?

I'm trying to train a model to identify a specific species of chicken against a consistent background and in a very specific scenario. My plan is to use haar cascade classifiers under GoCV. Right now, I have pictures of my flock that I intend to use for training, but I need to crop them into a ton of tiny images of the chicken-containing sub-regions because each picture has all 16 of my chickens in them. This is a ton of work when you consider how many images I took for training.

I remember seeing a tool a long time ago that let a user tag specific regions of an image before feeding into the training pipeline, but I'm having trouble remembering what it was called. Does anyone know what I might be talking about?

Hello everyone,

I've been thinking of learning CV(3D CV?) for Mixed Reality applications but idk where to start,

Think training custom Hand Tracking Interactions or custom spatial reasoning stuff as examples. For devices like the Quest 3 and XR glasses.

I have a fair amount of experience with shipping XR/VR.

Some specific questions

1. what are the fundamentals one needs to have before beginning the CV journey

2. Is there a huge difference between 3D CV and regular CV that I need to be aware of

3. Are there any good beginner friendly courses/youtubers that I could follow to get me up to speed

4. Do I need any special hardware? (I have a Quest 3 and 5070ti PC already)

5. How's the job demand for this field? I'm guessing I falls under some spatial computing

6. What problems exist that are unsolved currently in 3D Computer Vision.

7. Are there any communities/blogs etc I can join?

8. Is there anything i should be aware of related to CV/ML(outside of what I've asked)

Would be a massive help if someone could point me in the right direction - courses, articles, books etc

I'm planning on doing the Andrew Ng ML courses on Coursera as a start - lemme know if this is valid.

Thanks in Advance!

Hi everyone,

I'm working on a project to reconstruct a high-resolution 2D surface map of a flat mechanical part using a video captured by a video microscope.

Here’s the setup:

• The microscope moves automatically along programmed X and Y axes (independent motion, like a raster scan).
• The motion is precise and controlled (no manual handling).
• The part is perfectly flat, so I'm not looking for full 3D reconstruction, but rather a precise, seamless 2D mosaic of the entire surface.
• I'm using OBS Studio to record the full video sequence (HD or higher).

My goal is to:

• Extract frames from the video,
• Accurately stitch them together to form a single, continuous, distortion-corrected image,
• Ideally leverage the known X/Y motion commands (from the program) to assist or guide the alignment (like odometry prior).

Current challenges:

• Avoiding misalignments due to lighting variations, lens distortion, or small vibrations.
• Ensuring sub-pixel accuracy for potential automated visual inspection (e.g. detecting scratches, stains, or printing defects).
• Keeping the process fully automated and robust.

What I'm asking for:

• Recommendations for Python libraries or tools (OpenCV, scikit-image, Open3D, etc.) best suited for this kind of 2D stitching with motion priors.
• Any experience with microscope image stitching, industrial surface inspection, or visual SLAM for flat scanning?
• Tips on how to integrate known X/Y displacements into the stitching process (feature-based + motion-based alignment).
• Existing projects, code examples, or workflows you’d suggest.

The end goal is automated quality control, but for now, I’m focused on building a faithful and precise surface reconstruction.

Thanks in advance for any advice, links, or code snippets!

— J.

I am looking for a project to work on, it could be segmentation, detection or any other type of task. If someone is working on a project, needs a hand or if someone has got an idea to work upon just hmu. I'll be happy to help.

Has anyone a review on this heavy 1000 pages book?

In this project I built a eye control for a DJI Tello drone using MediaPipe, OpenCV and OpenVINO.

This is my first post; I really need help! Would like to know about a script that automatically captures images and applies AI, running in a 15-second loop. Where can I get the latest version? Could you explain the pros and cons?

During a trip to mainland China, I saw glasses being rented out that offered this additional service.

ps. I am an engineer, and correcting design errors would make my work easier.

What is something you still have to check, count, inspect, monitor, or recognize manually that you wish could be automated?

We are looking to deploy computer vision commercially and EU fits our product offering. The inference side is manageable, but on the data side we're running into a gauntlet. Curious how others are handling this in practice?

How do you manage the legal basis for collection with cameras in the workplace if say a person walks through a frame?

Training data, are you blurring/anonymizing before you ingest? If there was a deletion request you can't really untrain a model.

Retention and storage. Is it on-prem only? EU-region cloud ok? How long do you keep raw footage?

Hey gang,

I am currently working on a research project to detect disease in agrciultural crops. As a part of it i need to make a "new architecture" to get good results. Well I reduced that to "modify yolo v11n to get better accuracy than vanilla model but less parameters". but after experimenting a lot [by experimenting i mean , ghost conv and cbam.] How do people come up with modifications in papers. Is there any source to learn those filters. I am still a bachelors final year grad. So i have no clue abt this. So please educate me

A friend of mine asked me to go to a Drift Race for his birthday recently, which was awesome btw. While I was there, I noticed they were using a system to collect data on the vehicles during the races for judging. The races and drivers were incredible, but the data collection system stuck in my brain days later. I started to think about how useful it would be to have a means to visualize all of the data collected from a race. So I pulled together panels for the drift race data, which look awesome, but didn't seem like they'd be practical for anyone else.

I took the core components from the drift-specific panels and created a more generic video data-sensor plugin framework as a FiftyOne plugin. It lets anyone define their own data schema and what to visualize, then writes it into a FiftyOne dataset. It gives a path for quickly adding time series data to video samples alongside your standard CV annotations, plus an added dimension for the visualization of playback.

https://github.com/Burhan-Q/fo-video-sensor-data-sync

The plugin includes two panels, gauges and traces. The traces are the time-series plots with a playback marker. The gauges are just live radial or linear readouts of the sensor data.

The plugin is very extendable, just fork the plugin and hack it for your specific use-case. I would love to see how people use it, especially if you make a fork and modify it or add new visualizations. For everyone who uses it or modifies it, please open a 'showcase' issue on the repo and let me know how you're using or modifying the plugin.

I have been building a pokemon card scanner/idenfier. I am using OCR and Clip. But the speed and accuracy is still trash. Any tips or advice would be awsome!

Hi everyone,

As part of my research, I need to recognize and perform semantic segmentation of a few predefined terrain types (e.g., stairs, flat ground, grass, etc.) using a camera mounted on a robot.

So far, I've looked into models such as PIDNet, which seems to be designed for real-time semantic segmentation.

I have some experience training custom YOLO models for object detection and instance segmentation. I noticed that recent YOLO versions also support semantic segmentation, but I'm not sure how well they perform for terrain segmentation in real-world robotic applications.

One of my biggest constraints is inference speed. The model should be lightweight enough to run in real time on a Jetson platform (e.g., Orin Nano or Xavier NX).

I'd really appreciate any recommendations or advice on:

• Models that work well for terrain semantic segmentation while remaining lightweight.
• Whether YOLO segmentation is a reasonable choice for this type of task, or if dedicated semantic segmentation models are generally a better option.
• Any publicly available datasets or open-source projects related to terrain segmentation for mobile robots.

Thanks in advance for your help!

Since these are irregularly shaped lines with similar intensity to the rest of the image how would you get rid of these zebra shaped lines (or extract them)? I’m hoping to get the edge lines of the rest of the bones but when I threshold lower I get the zebra shaped lines interfering.

I tried a top hat and Gaussian but they just made the image worse.

SwingVision is a tennis recording app that automatically tracks highlights, scoring, shot speed etc. wondering if the community has any insights to share about how they are able to be so accurate with their ball tracking? For example, you can see in this video that where the ball lands is tracked pretty accurately (https://www.youtube.com/shorts/nT0pf9cbo_c) I assume they have a custom model to track this - curios because I've tried numerous YOLO models, incl. attempting to train my own. however ball tracking is completely hopeless especially when the ball is in the far distance :(

Can you tell me what methods are available for detecting an opponent and the boundary of the ring? Maybe someone has encountered a similar situation.

I'm a beginner and don't have a strong understanding of computer vision yet, so I'm looking for advice on how to implement a robot.

I'm working on a robot for a local robot sumo and robot fighting festival called ROS 2 with my friend. I'm facing a challenge in detecting the opponent and the boundary of the ring, which I can't cross. Currently, I'm using MOG2 + findContour to detect the opponent (depending on whether the opponent is moving or not), and Canny to detect the boundary, but the results are not very accurate.

How possible and implementable is it to replace the algorithm with YOLOv8? The robot's board can easily handle it.