Autonomous Aerial Surveillance AI System for Illegal E-Waste Detection and Environmental Forensics

Abstract

Electronic waste (e-waste) is one of the fastest-growing hazardous waste streams globally, with 62 million tonnes generated in 2022 and only 22.3% formally collected and recycled, leaving approximately USD 62 billion in recoverable resources unaccounted for (Global E-waste Monitor 2024). Illegal dumping, driven by high compliance costs and enforcement limitations, remains difficult to monitor due to its distributed nature across remote and inaccessible areas, particularly in regions with high import flows such as Europe.This paper presents GreenPolice, a human-centric, AI-driven aerial forensics system designed to autonomously detect, classify, and document illegal e-waste dumping using drone-based imagery. Built on the DJI Phantom 4 Pro V2.0 platform, GreenPolice integrates a custom deep-learning pipeline for multi-class e-waste detection (e.g., monitors, laptops, batteries, cables, PCBs). The system prioritizes human-AI collaboration through an operator dashboard for real-time review, validation, and annotation of detections, ensuring accountability and reducing false positives in diverse conditions.Each detection generates timestamped, geo-referenced metadata packages supporting chain-of-custody for regulatory enforcement.Preliminary experiments on an initial real-world dataset of ~168 annotated images, using the latest YOLO26 model trained on Roboflow, achieve [email protected] of 0.446, precision of 0.667, and recall of 0.509, validating feasibility with strong qualitative performance on cluttered scenes.This work represents phase 1 of the platform. Future phases will scale the dataset with synthetics (BlenderProc) and public benchmarks (e.g., AerialWaste [13]), add edge deployment, multi-spectral sensors, semi-autonomous planning, field testing, and blockchain logging for global operationalization.

Keywords: E-waste, Drone forensics, Computer vision, AI, Environmental monitoring, Human-AI Interaction, Autonomous systems, Synthetic data

DOI: 10.54941/ahfe1007188