YOLO Jobs

We are developing a drone-to-drone vision system using a Raspberry Pi 5 and a 40 TOPS AI Accelerator. The system must: Detect another drone using the camera feed Track the detected drone in real time Generate guidance commands to keep the target centered in view Support autonomous follow behavior Preprocess camera images for AI inference Train and optimize object detection models Optimize performance for low latency and high FPS on Raspberry Pi Required Skills: Python OpenCV YOLO/Object Detection Object Tracking (ByteTrack, DeepSORT, etc.) Raspberry Pi Edge AI Deployment Drone Systems and Navigation Preferred Experience: Drone vision projects Autonomous tracking systems Real-time AI inference on embedded devices Raspberry Pi AI accelerator deployment This is a guidance and developme...

I have a single 40-second MP4 clip that shows two motorcycles circulating the same track. Each bike can be separated at a glance because they are painted different colours. What I need is a reliable, frame-accurate measurement of the time interval between the first and the second motorcycle as they pass a chosen reference line on the circuit. Please use YOLO (or an equivalent real-time object detector) to: • detect both bikes throughout the whole sequence, • define a consistent reference line or region on the track, • timestamp the exact moment each bike crosses that reference, and • burn a clear visual overlay onto the video that displays the calculated gap in seconds. The finished deliverable is the processed video with the overlay already embedded; no separa...

I need a lean, working proof-of-concept that automatically counts foot traffic using a single 360-degree camera. The goal is to drop the unit into busy conference halls, festival entrances, or outdoor promotional zones and have it return reliable head-counts without manual intervention. Here is what matters to me: • Vision logic: Please build or integrate computer-vision models (OpenCV, YOLO, TensorFlow Lite or similar) that detect and track people moving through the camera’s full 360° field of view. The algorithm must distinguish unique passes so that every person is counted once. • Edge or cloud flexibility: I am fine with the model running on a Raspberry Pi 4, Jetson Nano, or a small cloud instance—as long as latency is low and setup remains simple. • ...