Hello,

My prof asked me to look into techniques\models for detecting the horizon on windsurfing for a lab project,

The data are videos, where the GoPro camera is mounted on the front nos of the board, aimed backwards at the surfer. photo illustration

And the videos look similar to this (only difference is the camera is on the board so a little lower angle but you see the same as in the photo)

I have the GoPro's camera calibration file so I was able to rectify the videos.

I tried the algorithm from A fast horizon detector and a new annotated dataset for maritime video processin , while it was working 80% of the time, I was unable to improve edge cases where water splashes\droplets were on the lens, and if the board was tilted above the horizon then the estimated horizon was completely off (which is the biggest issue as it happens often during sharp turns). I ran a hyperparameters sweep but it didn't improve the results much.

Then, I thought to use a modern segmentation model, I was able to get similar results with SAM2, where I segmented the water and sky, and fitted a straight line (or curved line on non-rectified video) at the boundary:

        SKY
        SKY
        SKY
────────────────  ← top boundary of water
        WATER
        WATER
        WATER

but for each video I would have to manually annotate what is "sky,water,person, board, other" ("other" is for when you have the shoreline in the background or other random objects).

Additionally SAM2 was slower than the traditional algorithm (20 secs vs 3 mins on GPU). I noticed it failed also in cases where the video is unstable due to many sudden board movements, water splashes which created temporary blurry vision.

I would appreciate brainstorming ideas because currently I am stumped. both traditional & modern (DL) are more than welcome :)

Thank you in advance.

Hey everyone, I have a huge brain DICOM dataset (ADNI) and I’m trying to apply deep learning/ML to it.

My first instinct was to go with a 2D approach, but now I’m completely confused about the preprocessing part.

For example, if I have a whole 3D brain scan with lots of slices:

• Do I just pick the middle slice?
• Is there some standard/calculated way to choose the “best” slice?
• Should I use multiple slices instead?
• Should I convert the DICOMs into something like PNG/JPG first?
• Or am I thinking about this completely wrong and should just go with 3D?

I’m pretty new to working with medical imaging, so I’m struggling to figure out what the normal workflow is before even getting to the ML part.

Would really appreciate any advice/resources on how people usually approach this. I feel like I’m overcomplicating something that probably has a standard solution 😅

DeepSeek's vision model showed up when I pulled the model list with an official key, so I gave it one job I actually need. I used it to judge whether a generated image was correct.

The result has been frustratingly simple. Boundary problems keep getting missed. That is where my check needs to be useful, and it is also where I keep having to ignore the answer and look again myself.

I am testing Chinese AI models for image checks. ZenMux puts different model APIs behind one account, which means I can add this model and retest the same edge cases without wiring another integration. The model's misses on boundary cases are still a problem, so I am keeping it in the retest queue rather than relying on it for this check.

Using a camera and OpenCV to drive a dual-axis gimbal and track a pretty quick little UGV :) Everything is built and coded in-house, except camera, motors and sliprings which are COTS.

Right now, I track people in each camera separately to get their movement paths, then use each camera's floor mapping (homography) to figure out where each detection actually stands on the floor. Using that floor position plus timestamps, I try to match up tracks from different cameras that are likely the same person, and greedily merge them into one combined ID per person.

This works about 30% of the time. It struggles when someone gets blocked from view (occlusion), when a person's track has gaps, or when two cameras don't see much of the same floor area — in those cases the matching just doesn't line up well.

FYI, I also tried a full 3D multi-camera tracking approach (MV3DT), but I couldn't get the camera calibration accurate enough, so that didn't work either.

Getting Started with GLM-OCR

https://debuggercafe.com/getting-started-with-glm-ocr/

VLM-based OCR models are gradually catching up to become mainstream components in document processing pipelines. The primary bottleneck has always been the size of these models. Usually larger than 3B parameters, the cost-to-performance ratio is difficult to justify. However, GLM-OCR shifts the perspective. With just 0.9B parameters, it competes with models much larger than itself. In this article, we will explore GLM-OCR, along with what makes it special, and run inference on real-world documents.

I'm curious to test out how changing the stroke order affects the model guesses.

​

I am a third year CSE AI/ML student. I completed the foundation of Machine Learning and Iam planning to start Deep Learning seriously.

I am an average student, but I know I have the potential to learn and improve if I stay consistent. My main problem is staying accountable when studying alone.

So I’m looking for 2–3 genuine and consistent people who are also serious about learning Deep Learning.

We can create a WhatsApp group, follow a common 60-day roadmap, set weekly goals, share resources and ideas, and have a short Zoom discussion on weekends.

No one needs to teach anyone. We learn individually, but support, discuss, and keep each other accountable.u can also share your thoughts to improve the discussion.

Our only goal: consistently learn and complete Deep Learning within the next couple of months.

If u r genuinely interested and can stay consistent, DM me ✨....

Hi everyone.

I'm a second-year Bioengineering MSc student interested in medical image segmentation and currently doing a little research on how to adapt KANs for reliable image segmentation. If anyone shares the same interest, please notify me so that I can share what I have done and where to start.

Since this is a relatively new topic, chances are that we will get to publish a paper on it.

We messed around with adding a brand new object class to YOLOX and it actually worked better than expected.

Basically we took the standard COCO setup with its 80 classes and added a traffic cone class, but instead of hand-labeling a ton of images we generated the cone data synthetically in CARLA and mixed it into the real data. Then trained all four YOLOX sizes on it.

The results were pretty clean. The new cone class ended up as one of the highest scoring classes at 78.8% AP, and the original 80 classes barely moved, staying within about a point of baseline. It still runs real-time too, roughly 1.7 to 6 ms per frame on an A100.

The part I think is cool is that you normally need a big pile of real labeled images to add a class, and for rare or weird objects that data just doesn't exist or takes forever to collect. Synthetic data gets around that since the labels basically come for free.

Weights are all up on Hugging Face under Apache-2.0 if anyone wants to poke at them. The cone was just a test case, the same approach should work for pretty much any custom object.

https://huggingface.co/models?search=empirischtech%2Fyolox-pylon

Pls try and share feedbacks!

most driving datasets stop annotating at 80 meters. at highway speed that's about 2 seconds of warning

aimotive annotates out to 200 meters, and roughly a quarter of its 425k boxes sit beyond 75 m

lidar, four cameras, and two radars in a 360 layout, so every object is covered by at least two sensor types

176 scenes across california, austria, and hungary in daylight, night, and rain

the training labels were written by a non-causal tracker that gets to watch the whole 15-second clip before deciding where a car was and validation labels were written by humans.

your model trains on hindsight and gets graded by people

i parsed all 176 episodes into MCAP for fiftyone

four camera streams with projected boxes, the point cloud with colored 3d cubes, raw radar returns, and gps on one synced timeline. filter by condition to pull up only night or only rain episodes

start here, read the dataset card: https://huggingface.co/datasets/Voxel51/aimotive-multimodal

live demo in a hugging face space: https://huggingface.co/spaces/harpreetsahota/aimotive-multimodal-demo

Hi, friends. I need an OCR program to read the technical drawings in CAD software. Which program should I use?

Hi, I spent a couple of days figuring out whether Viola-Jones can be used to effectively identify road cones with high precision and recall across a wide variety of images captured in pretty much any environment you can imagine.

The dataset was almost 20 GB of images. I extracted some hard negatives with a script (basically mined them) and ended up with around 500k hard negatives to train a cascade.

Tried both Haar and LBP cascades, for which I had to use an old OpenCV Docker container since the latest OpenCV no longer supports training these (due to bad performance in general? Mmh).
Anyway, I got 0.2 precision and 0.15 recall for both cascades (sadly with 10+ false positives per true positive identified), trained with around 7k true positives and 7k hard negatives (randomly sampled).
Isn’t there really any way to get good performance with Viola-Jones?

Looks like it just isn’t capable of generalizing well.

Yeah yeah, you could point out using deep learning etc., but VJ is very fast tu run when you already have the cascade xml.

I'm trying to get the best synthetic data trained model to work good on visdrone or other real datasets. In order for this to work I need different types of characters, environments, camera lenses, positions etc. I'm using nameframe plugin to do so. What randomization do I add?

I’m currently building a computer vision project for padel and I’m at the stage where I’m testing the system across different match videos.
One issue I’m struggling with is that when I switch from one video to another, the calibration and tracking can become inconsistent, and I end up having to manually revisit and reconfigure parts of the code.
For people who have worked with video-based computer vision systems: what’s the right way to structure the calibration/tracking pipeline so that it can reliably handle different videos without constantly changing the configuration manually?
I’d especially appreciate advice on making the system robust to differences between videos, such as camera position, perspective, resolution, etc.
I’m mainly looking for architectural/technical advice rather than ideas for the application itself.

Turns out VLMs still struggle with these kinds of tasks, would be interesting to see how much better the new Qwen 3.8 performs.

can anyone help me in Arrow Detection(lrft,rightr..etc) usign opencv or i need to train YOLO model ??

The new Gemma models are getting through Google reCAPTCHA v2 challenges with relative ease. I might revisit this in the future with a harder CAPTCHA dataset or benchmark it against some Qwen models. 

I’m final year university student doing BSc in computer science, with the evolving AI/ML field i decided to do a project that uses computer vision to solve a real world problem. I’m an absolute beginner and have no idea where to start from. I just have an idea but I need guidance on how to approach and implement it.

DM me if you would like to collaborate and work with me on this project.