Computer Vision-Based Waste Detection and Classification for Garbage Management and Recycling

Islam, Shahid; Alam, Md. Golam Rabiul

doi:10.1007/978-981-19-8032-9_28

Cited by 2 publications

(1 citation statement)

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“…The study in Reference 11 offers a deep learning‐based system for detecting and classifying diverse trash kinds in order to facilitate recycling. With a large dataset and a real‐time detection framework, the proposed technique surpasses previous approaches, obtaining a mAP of 66.08% and an inference speed of 55–58 ms on pictures and videos, successfully tackling waste separation concerns.…”

Section: Related Workmentioning

confidence: 99%

Efficient solid waste inspection through drone‐based aerial imagery and TinyML vision model

Malche,

Maheshwary,

Tiwari

et al. 2023

Trans Emerging Tel Tech

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Solid waste management is a significant challenge in the development of smart cities. Existing approaches for solid waste monitoring are often time‐consuming and resource intensive. Therefore, this study proposes a novel approach to solid waste monitoring that utilizes drone technology. The proposed method enables the efficient identification and classification of waste objects in the garbage discovered by the drone. This system can inspect every part of a smart city from a remote location, allowing for the timely and effective management of solid waste. Thus, the proposed system can be easily integrated in the existing waste management system for smart city. The drone‐based solid waste monitoring system comprises a drone equipped with a computer vision model for resource‐constrained devices and a software application that operates the drone and analyzes the captured image or video. The system utilizes the Internet of Things (IoT) to upload the collected data to the cloud, making it easily accessible whenever necessary. The proposed drone‐based solid waste monitoring system is a promising solution for the efficient and cost‐effective management of solid waste in smart cities. The system's innovative use of drone technology and IoT provides a scalable and adaptable solution that can be customized to meet the needs of any city.

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Section: Related Workmentioning

confidence: 99%