TY - JOUR
AU - 
AB - Computer Vision; Classification of waste for recycling has been a focal point for scientists interested in the field of Object Detection; conservation of the environment. Recycling consists of numerous steps, of which one of the most Classification; crucial is the segregation of recyclables from all other waste. Due to a lack of safety standards in developing countries, waste collection is often done manually by domestic helpers, or "rag-pickers". Recycling; Such a process risks individual and public health. The waste collection methods may ultimately Waste Disposal. cause waste to become non-recyclable due to cross-contamination. Literature shows that research in this direction focuses on a single class of waste detection. The proposed work investigates CNN, YOLO, and faster RCNN-based multi-class classification methods to detect different types of waste at the collecting point. The smart dustbin proposed employs these computer vision methods with a Article History: Raspberry Pi microcontroller and camera module. The experimental results for multi-class classification show that the CNN has 80% of accuracy with 60% of the loss. Whereas the YOLO Received: 29 November 2021 algorithm shows an accuracy of 88% and a loss of 40%. But the best results were obtained from faster RCNN object detection with API, 
TI - Development of Computer Vision Algorithms for Multi-class Waste Segregation and Their Analysis
JO - Emerging Science Journal
DO - 10.28991/esj-2022-06-03-015
DA - 2022-04-19
UR - https://www.deepdyve.com/lp/unpaywall/development-of-computer-vision-algorithms-for-multi-class-waste-VRTxO4lK03
DP - DeepDyve
ER -