Plug-and-Play Edge–Cloud Architecture for Autonomous Urban Garbage Detection with Self-Supervised Active Learning

Urban waste accumulation in rapidly expanding Indian cities represents a persistent public health and governance crisis, compounded by the absence of scalable automated monitoring and the growing gap between waste generation and scientifically processed disposal. Existing AI-based waste detection systems are trained predominantly on Western datasets, require dedicated camera infrastructure, lack mechanisms for autonomous post-deployment improvement, and offer no accountability layer between detection and confirmed municipal action. This paper presents a plug-and-play dual-model edge-cloud waste intelligence framework that connects directly to existing street surveillance infrastructure without requiring any hardware replacement. A 2.9 MB YOLOv11n model runs real-time inference on resource-constrained edge devices — Raspberry Pi with under 500 MB RAM and no GPU — while a cloud-hosted verification model crossvalidates detections, performs multi-class waste classification, and autonomously retrains the edge model weekly using cloud-corrected samples, eliminating continuous human annotation from the edge retraining loop entirely. The framework additionally counters illegal littering through timestamp-anchored detection logs, forecasts regional waste accumulation for proactive municipal planning, automates recycling coordination, and exposes a citizen-facing transparency platform that publicly reports the full lifecycle of each garbage event from detection to confirmed resolution. Initial experiments on TACO and TrashNet datasets yield [email protected] of 45.8% and [email protected]:0.95 of 23.6% with an F1-optimised confidence threshold of 0.184, and real-time inference is confirmed on Raspberry Pi hardware under continuous operation without lag. The performance gap observed on Indian street conditions motivates an ongoing Punespecific dataset collection effort whose domain adaptation results will be reported as the dataset matures. This architecture simultaneously addresses cost, scalability, domain specificity, legal deterrence, and governance transparency — making it directly deployable within Indian smartcity programmes at a fraction of the cost of purpose-built monitoring networks.