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I need a compact, fast object-detection model that runs directly on an edge board (Jetson Nano, Raspberry Pi, Coral or similar) and processes aerial images from drones. The immediate application is surveillance, yet the solution should stay flexible enough to be reused later in agriculture or disaster-response scenarios. Source imagery will contain a mix of people, cars, buses, bicycles and assorted infrastructure. The model must be especially reliable at spotting people and critical infrastructure elements, while still recognising the wider vehicle classes.I am open to any justified architecture (YOLOv8, MobileNet-SSD, EfficientDet-Lite, or superior alternatives)—provided it outperforms YOLOv9c and similar models while delivering real-time inference on edge devices once quantized o...

We are looking for an experienced Android Native developer to fix critical bugs in an already developed application. This is not a fresh development project. The application is already developed, but it currently has issues related to the YOLO integration and/or Android implementation that need to be fixed. The app is built using: Kotlin (Android Native) YOLO (You Only Look Once) object detection model AI-based processing within the mobile app Your first task will be: Identify and fix bugs related to YOLO / model integration / app behavior Ensure stable performance after fix If you successfully resolve the issue, we will assign you the complete long-term development project, as this is an ongoing product. Importnat Note: For now we will not be able to share the code so developer needs ...