I need a working proof-of-concept that spots drones in real time by blending vision and RF cues. On the vision side you will train and fine-tune YOLOv8 with Anti-UAV and VisDrone footage. In parallel, spectrograms coming from DeepSig RadioML together with my own SDR captures (HackRF / RTL-SDR) should feed a CNN that flags drone-class emissions. Once both streams run reliably, I want them merged—either with a straightforward rule set or a small neural fusion layer; I’m happy to discuss which choice achieves the best balance of speed and robustness. The finished model must be benchmarked on four fronts that matter equally to me: Accuracy, Precision, Recall, and False-Alarm Rate. Because the project will eventually guard a sensitive site, I’m aiming for a solution that can move from laptop to field hardware without major rework. Python, PyTorch, and standard CV/RF libraries are expected; please keep your code clean, well-commented, and reproducible. Deliverables: • Trained YOLOv8 weights and training notebook • Trained RF-CNN model and training notebook • Fusion module with documented logic or architecture • End-to-end inference script that ingests live or recorded video plus I/Q data and outputs detection events • Performance report covering the four metrics above, including confusion matrices and a short discussion of failure cases Timeline is tight—I’d like initial results as soon as possible, so only reply if you can start right away and iterate quickly.