Smart Waste Classification System (SWCS)

Overview

SWCS is a compact, offline-capable recycling station for small venues — cafés, office pantries, labs, school micro-recycling points — where there's no staff to watch the bin and no network to rely on. It closes the full loop from sensing → recognition → decision → physical sorting, not just classification on a screen.

It was my final-year project for ELEC S411F (Electronic & Computer Engineering Project, 2025/26), supervised by Dr. Tong Kwong Bun Bruce.

Demo

The problem

Recycling quality collapses when materials are contaminated at the source. Most academic work stops at lab accuracy, and most commercial "smart bins" are large, expensive, network-dependent installations for big facilities. There's a real gap for something small, cheap, robust and offline that can actually act on its decision — and safely refuse to act when it isn't sure.

Approach

A two-stage edge-vision pipeline plus a confidence-gated controller:

• Localize — YOLOv8 detects the item and crops the region of interest (ROI).
• Classify — MobileNetV3-Large labels the ROI as paper / plastic / metal / glass / cardboard. Chosen for edge latency (not just accuracy) and deployable via ONNX / TorchScript / TensorRT / CUDA.
• Decide — a Decision Engine fuses the model probabilities and applies a confidence gate: divert only if the class and its confidence clear a threshold; otherwise the item stays on the default bypass route. Low-confidence cases never trigger a wrong physical action.

System architecture

• Upper machine: NVIDIA Jetson Orin Nano runs camera capture, YOLO localization, ROI cropping, classification and the Decision Engine.
• Lower machines: two Arduinos — belt_arduino (IR trigger + conveyor) and servo_arduino (sorting flap) — for clean separation of control.
• Protocol: USB-serial JSON with an identity handshake between the Jetson and each Arduino.
• Closed loop (FSM): IR sensor detects an item → stop belt → capture frames → YOLO + MobileNetV3 + confidence gate → JSON command → servo flap rotates → belt resumes → cycle logged with image, prediction, confidence, action and timing.

Training used PyTorch + PyTorch Lightning (OmegaConf config, torchvision ImageFolder) on a self-collected dataset that ranged from clean backgrounds to cluttered, real-world, varied-lighting scenes to harden against contamination and occlusion.

Results

A labelled 120-cycle closed-loop experiment (60 metal + 60 non-metal), using metal routing as the physical test case:

• Offline classifier accuracy: 93.59% — metal 93.69% precision / 95.59% recall / 94.63% F1
• System routing accuracy (2-class): 90.8% — metal precision 94.5%, recall 86.7%
• False diversion: 5.0% · Missed diversion: 13.3%
• Mean end-to-end cycle: 3.29 s — of which recognition was only ~61 ms

What I learned

• The AI was never the bottleneck. Recognition took ~61 ms; the full cycle ~3.29 s, dominated by servo actuation (~2.7 s) and conveyor recovery. The hard engineering was mechanical timing, not inference.
• Closed-loop accuracy depends on the actuator, not just the model. A high-torque servo caused PWM jitter and unstable flap angles — a correct classification could still mis-sort. An independent 5 V supply with a common ground to the Arduino removed the jitter entirely.
• Confidence gating is a trade-off. It kept false diversion low (5%) but raised missed metal diversions (13.3%) — a knob to tune against labelled system-level data, not just offline validation.

Further work

Multi-path routing for all five materials (not only the metal test path), latency reduction in the mechanical chain, and long-duration reliability testing — camera drift, serial stability, belt misalignment, servo wear.