Low-Cost and Lightweight Campus All-Weather Dual-Optical UAV Detection and Active Warning System

To address the challenges of unauthorised drone flights in campus low‑altitude security—where traditional detection equipment is costly and ineffective at night—this paper designs a lightweight, all‑weather drone detection and early‑warning system based on a Raspberry Pi edge computing platform and visible/infrared dual‑spectrum fusion. The system uses an IMX219‑77IR infrared camera that automatically switches imaging modes according to ambient brightness, achieving day‑and‑night continuous perception. A YOLOv8n model is compressed to 10 MB via channel pruning and knowledge distillation, reaching an inference speed of 85.2 ms/frame on the Raspberry Pi. A self‑built campus drone dataset of 2,000 images (1,600 open‑source + 400 self‑collected) yields 97.16% precision, 93.79% recall, and 97.27% mAP50. A Flask backend and web map interface provide real‑time alerts by polling every 2 seconds. Total hardware cost is below 1,500 yuan, more than 70% lower than traditional systems. Field tests (5–50 m) show daytime confidence >0.9, nighttime infrared confidence ≈0.88, false negative rate <8%, false positive rate <5%, and stable continuous operation. The project has won a university‑level competition second prize and its software copyright application is under review, demonstrating strong practical value and promotion potential.