MUMBAI, India, Aug. 17 -- Intellectual Property India has published a patent application (202641095521 A) filed by S Singaravelan; G S Arun Kumar; M. Ramya; Dr. M. Vargheese; N. Rigana Fathima; Dr. G. Jemilda; Priyadharshini R; Praveen Kumar J; Saravanan N; P. Vinothini; Dr. M. Ramanan; and Dr. Aravind B on August 06, 2026, for Deep Learning-Based Waste Segregation And Recycling Automation System.
Inventors include S Singaravelan; G S Arun Kumar; M. Ramya; Dr. M. Vargheese; N. Rigana Fathima; Dr. G. Jemilda; Priyadharshini R; Praveen Kumar J; Saravanan N; P. Vinothini; Dr. M. Ramanan; and Dr. Aravind B.
The application for the patent was published on August 14, 2026, under issue no. 33/2026.
Abstract: The present invention relates to an intelligent waste segregation and recycling automation system based on deep learning for automated identification, classification, and sorting of waste materials. The system comprises an image acquisition module configured to capture images of waste using one or more RGB, infrared, depth, or hyperspectral cameras; a preprocessing module for image enhancement, segmentation, and feature extraction; and an artificial intelligence-based classification module employing one or more deep learning models, including Convolutional Neural Networks (CNN), YOLO, Vision Transformers (ViT), ResNet, MobileNet, EfficientNet, or hybrid machine learning algorithms, to classify waste into biodegradable, recyclable, hazardous, electronic, plastic, paper, glass, metal, textile, biomedical, and non- recyclable categories. The system further incorporates one or more sensing modules, including weight, proximity, ultrasonic, moisture, gas, RFID, metal detection, or spectroscopic sensors, to improve classification accuracy through multimodal sensor fusion. A programmable control unit receives classification outputs and actuates automated sorting mechanisms comprising conveyor belt diverters, robotic manipulators, servo-operated gates, pneumatic actuators, suction grippers, magnetic separators, or autonomous mobile platforms for directing waste into corresponding collection bins. The system may further include Internet of Things (IoT)-based communication, edge or cloud computing, real-time monitoring, predictive maintenance, adaptive model retraining, recycling analytics, fill-level monitoring, route optimization, and data logging for efficient waste management and operational control. The invention enables continuous learning through model updates to improve classification performance across diverse waste types and environmental conditions. By reducing manual intervention, increasing segregation accuracy,improving recycling efficiency, minimizing contamination, lowering operational costs, and supporting sustainable resource recovery, the disclosed system provides a scalable, intelligent, and automated solution suitable for residential, commercial, industrial, institutional, healthcare,municipal, and smart city waste management applications.
Disclaimer: Curated by HT Syndication.
