Smart Waste Segregation System Using Real-Time Object Detection and Image Segmentation – Final Year Project

Abstract

The exponential growth of municipal solid waste poses severe environmental and health challenges worldwide. Traditional manual waste segregation is labor‑intensive, error‑prone, and inefficient. This project proposes a Smart Waste Segregation System that leverages state‑of‑the‑art computer vision techniques—specifically YOLOv8 for real‑time object detection combined with a DeepLabV3+ segmentation model—to automatically identify, classify, and separate recyclable, organic, and hazardous waste items on a conveyor belt. The system integrates with an IoT‑enabled smart bin that actuates mechanical sorting arms based on model predictions, providing a scalable, low‑cost solution for smart cities and industrial waste management facilities.

Project Overview

The system comprises three primary modules: (1) a high‑resolution camera array capturing continuous video streams of waste items on a moving belt; (2) a dual‑pipeline inference engine where YOLOv8 rapidly detects coarse waste categories (plastic, metal, paper, organic, hazardous) and a Segmentation model refines the mask for overlapping or partially occluded objects; (3) a microcontroller‑driven actuator network that physically routes each item into its designated bin. The software stack is built on Python, leveraging OpenCV for image preprocessing, PyTorch for model inference, and MQTT for real‑time communication with the hardware layer. The project includes a web‑based dashboard for monitoring system performance, visualizing detection results, and logging operational metrics.

Problem Statement

Municipalities and industrial plants generate tons of mixed waste daily, yet only a fraction is correctly sorted for recycling or safe disposal. Human operators suffer from fatigue and exposure to hazardous materials, leading to misclassification rates exceeding 30% in many facilities. Existing automated solutions are either prohibitively expensive or limited to single‑category detection, lacking the ability to handle complex, overlapping waste streams. There is a pressing need for an affordable, accurate, and visually demonstrable system that can operate in real‑time under varying lighting and waste composition conditions.

Proposed Solution

The proposed architecture employs a two‑stage vision pipeline. First, YOLOv8 (pre‑trained on the COCO dataset and fine‑tuned on a custom waste dataset of 12,000 images) provides bounding boxes and coarse class probabilities within 15 ms per frame on an NVIDIA Jetson Nano. Second, for each detected bounding box, a cropped region is fed into a DeepLabV3+ model (ResNet‑101 backbone) to generate pixel‑level masks, enabling precise separation of items that are stacked or partially hidden. The inference engine runs on the Jetson Nano using TensorRT optimizations for sub‑30 ms latency. Detected classes trigger corresponding GPIO signals to control servo‑driven sorting arms. A fallback rule‑based system ensures that ambiguous items are directed to a “review” bin for manual handling.

Expected Outcomes

The final deliverable includes a fully functional prototype capable of processing at least 10 kg of waste per hour with an overall classification accuracy above 92% and segmentation IoU > 0.85 for overlapping objects. The system will generate real‑time visual dashboards displaying detection overlays, confidence scores, and sorting statistics. Comprehensive documentation—including hardware schematics, software source code, model training scripts, and a user manual—will be provided. The project will also produce a comparative analysis report benchmarking the dual‑pipeline approach against single‑model baselines.

Research Papers & References

• Real‑Time Object Detection for Waste Management Using YOLOv5 – IEEE Access, 2022.
• DeepLabV3+ for Precise Segmentation of Overlapping Objects in Industrial Environments – International Conference on Computer Vision (ICCV), 2021.
• Edge AI for Smart Cities: Deploying Vision Models on Jetson Platforms – ACM Transactions on Embedded Computing Systems, 2023.
• IoT‑Enabled Automated Waste Sorting: Design and Implementation – Sensors Journal, 2020.
• Benchmarking Real‑Time Vision Models on Low‑Power Devices – IEEE Transactions on Neural Networks and Learning Systems, 2021.