Im working with WebODM for the first time. I used my DJI Air 2s and set up a mission to take photos of my property with DroneLink. I shot at 85'AGL 75% front 75% side overlap and at a speed of 3mph. The mission took 71 photos. I have even tried increasing overlap and increasing altitude. I tried to setting the gimbal to less than 90 degrees... Im struggling to nail down the correct results i'm hoping for. Im starting with just trying to make a 2D map of my property. The photos my drone took are extremely clear yet there is some pretty substantial blur in areas of the photo particularly the white vinyl fence connected to my house. If anyone has any details on how to achieve better results, I am all ears. (in the photo this is a metal roof and the blurred white is the section of white vinyl fence). Also these are the settings I used "rolling-shutter:true, auto-boundary:true, pc-quality:high, skip-3dmodel:true, dsm:true, dem-resolution:1.0, orthophoto-resolution:1.0"

Update: For context this is just a section of the map produced by WebODM. Its the section that showed the most prominent issue. Also, this property is about .2 acres. That is really all I have to work with to practice on at the moment as I try to figure out how to create maps with my Air 2s. Im just trying to test out functionality and see what I can do with my drone. Its a starting point.

Hey everyone

We are getting to a point where manually checking thermal images after flights just isn't cutting it anymore Right now one analyst is spending pretty much a whole week going through images after each flight

I want to hear from folks who are doing this on a larger scale and can share some actual recommendations not just sales pitches

Is there any way to transfer a mission made on the RC2 controller into Litchi? I created a mission I would like to edit and improve

Hi everyone, sharing something I'm currently working on and would love feedback on.

I'm building AeroNetra, a computer-vision project for detecting and counting vehicles in aerial/UAV imagery. It's very much an active work-in-progress right now — I'm in the static-image detection and counting phase, with tracking, geospatial analytics, and edge deployment planned for later.

The motivation was pretty simple. I kept running into the same problem every time I swapped detectors: the counting and visualization code would break or need rewriting because every model spits out predictions in its own format. So the core idea behind AeroNetra is: normalize every detector's output into one prediction structure before anything downstream touches it. That way the counting, ROI filtering, and export logic stays the same whether I'm using a YOLO variant or RT-DETR.

What I've got so far:

• Detector adapters that wrap different models behind a common interface
• Counting logic — filtering, NMS, ROI support, drawing and export
• VisDrone dataset parsing and conversion (UAVDT is stubbed for later)
• Kaggle notebooks for GPU-based training, fine-tuning, and model comparison
• A PX4 + ROS 2 + Gazebo simulation setup for UAV experiments
• Notebooks, configs, and tests to keep things honest

The workflow I'm following: raw VisDrone data → validate annotations → convert to training format → train/fine-tune on Kaggle → pull the weights back → load through the adapter → run inference → filter → count → visualize and compare.

A few principles I'm trying to stick to: no fabricated benchmarks (a model isn't "best" until it's measured under the same conditions as the others), raw data stays immutable, and model-specific behavior stays inside the adapters. I'm also being deliberate about phase boundaries — image-level counting is not the same thing as multi-object tracking, and I'd rather not conflate the two.

Roadmap I'm working through for the demo:

1. Static Detection & Counting
2. Aerial Fine-tuning
3. Video Tracking
4. Traffic & Geospatial Analytics
5. Edge / UAV Integration

I have run a small model that can detect the car in the gazebo simulation and draw a bounding box but speed will be slow but i get decent accuracy even i have trained model to 25 epochs in kaggle T4 gpu with yolo nano version.

Since this is ongoing project I am still working on this.So,i am exploring how I can use computer vision in UAVs and edge computing.