An Accuracy of Identifying Recyclable Objects and the Number of Objects Identified from Municipal Waste Without Occlusion Using Computer Vision Techniques DOI

S. Menaka,

A. Gayathri

Published: Jan. 1, 2024

The proper disposal and recycling of waste products are critical concerns for municipalities worldwide. In recent years, the development machine learning algorithms has led to automate identification separation recyclable objects from non-recyclable objects. effective management municipal become a challenge in modern urban environments. This study addresses task accurately identifying determining their quantities within streams, leveraging advanced computer vision techniques. objective is develop robust system capable detecting while overcoming challenges posed by occlusions. proposed approach combines state-of-the-art object detection with innovative occlusion handling methods achieve accurate items complex compositions. offers promising avenue enhancing practices, providing actionable data informed decision-making sustainability.

Language: Английский

Towards Scalable and Cost-Effective Design for Intrusion Detection for IIoT Environment Using Metric Active Learning DOI

S. Menaka,

B. Ahalya,

Saurabh Sharma

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 29 - 46

Published: Jan. 1, 2025

Language: Английский

Citations

0
Envision, Enhanced, Envisage (EEE) IoT Device Prediction Using Neural Network DOI

S. Menaka,

Siddharth Biswal,

P. Teja

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 104 - 116

Published: Jan. 1, 2025

Language: Английский

Citations

0
An Accuracy of Identifying Recyclable Objects and the Number of Objects Identified from Municipal Waste Without Occlusion Using Computer Vision Techniques DOI

S. Menaka,

A. Gayathri

Published: Jan. 1, 2024

The proper disposal and recycling of waste products are critical concerns for municipalities worldwide. In recent years, the development machine learning algorithms has led to automate identification separation recyclable objects from non-recyclable objects. effective management municipal become a challenge in modern urban environments. This study addresses task accurately identifying determining their quantities within streams, leveraging advanced computer vision techniques. objective is develop robust system capable detecting while overcoming challenges posed by occlusions. proposed approach combines state-of-the-art object detection with innovative occlusion handling methods achieve accurate items complex compositions. offers promising avenue enhancing practices, providing actionable data informed decision-making sustainability.

Language: Английский

Citations

2