Autonomous Drone Thermal Surveillance: Edge-Optimized YOLOv8 Detection, ByteTrack Kinematics, and Heuristic Threat Classification for Border Security

Continuous perimeter and international border monitoring presents significant operational vulnerabilities when relying on human sentries or conventional electro-optical visible sensors, particularly under zero-lux darkness, dense fog, and optical clutter. Aerial thermal long-wave infrared (LWIR) sensing mitigates optical illumination dependencies; however, uncalibrated infrared imagery suffers from thermal blooming, sensor non-uniformity, low dynamic contrast, and an absence of chromatic cues. In this paper, we propose a comprehensive, edge-deployable aerial thermal surveillance architecture developed for drone-based perimeter defense. The framework combines: (1) dynamic Contrast-Limited Adaptive Histogram Equalization (CLAHE) to amplify localized radiometric temperature gradients; (2) a lightweight YOLOv8 nano (YOLOv8n) backbone fine-tuned on FLIR thermal datasets for rapid person and vehicle detection; (3) ByteTrack association for persistent multi-object tracking across occlusions; (4) rule-based behavioral kinematics to identify anomalous postures (crouching, crawling, coordinated group motion); and (5) a dual-stage false-alarm suppression mechanism distinguishing non-human thermal emitters from genuine perimeter breaches. Benchmark evaluation demonstrates an end-to-end inference latency of 2.0 ms per frame (~500 FPS) on consumer edge GPU hardware (NVIDIA RTX 4050, AMD Ryzen 7 7000 Series), enabling fully autonomous, real-time local execution without cloud reliance. The complete system is deployed via an interactive Streamlit and TypeScript surveillance terminal. This work was awarded First Prize at the Hacker's Cult NSUT Hackathon (AI-3 Track: Computer Vision / Security Tech), Netaji Subhas University of Technology, New Delhi.