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Related Concept Videos

Aggregates Classification01:29

Aggregates Classification

366
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
366

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Updated: Aug 30, 2025

Detection and Recovery of Palladium, Gold and Cobalt Metals from the Urban Mine Using Novel Sensors/Adsorbents Designated with Nanoscale Wagon-wheel-shaped Pores
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Nature-Inspired Search Method and Custom Waste Object Detection and Classification Model for Smart Waste Bin.

Israel Edem Agbehadji1, Abdultaofeek Abayomi2, Khac-Hoai Nam Bui3

  • 1Honorary Research Associate, Faculty of Accounting and Informatics, Durban University of Technology, P.O. Box 1334, Durban 4000, South Africa.

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|August 26, 2022
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Summary
This summary is machine-generated.

This study introduces a smart waste bin system using the You-Only-Look-Once (YOLO) convolutional neural network for accurate waste classification. The Yolov3 model, combined with a nature-inspired learning rate, demonstrated superior performance in detecting and sorting waste at the point of collection.

Keywords:
Internet of Things (IoT) enabledKestrel-based search algorithm (KSA)You-Only-Look-Once (YOLO)convolutional neural network (CNN)object detection and classificationsmart bin

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Environmental Science

Background:

  • Global waste management challenges necessitate innovative solutions for effective collection and sorting.
  • Existing waste bins lack the capability for at-source solid waste segregation, impacting overall management efficiency.
  • The South African University of Technology (SAUoT) is leveraging emerging technologies for advancements in solid waste management.

Purpose of the Study:

  • To develop and evaluate a smart waste bin system capable of classifying waste at the point of collection.
  • To employ the You-Only-Look-Once (YOLO) object detection algorithm for accurate waste categorization.
  • To investigate the efficacy of nature-inspired algorithms for optimizing the learning rate in convolutional neural network (CNN) models for waste detection.

Main Methods:

  • A custom YOLO model was developed and trained for waste object detection, utilizing various weights and backbones (darknet53.conv.74, darknet19_448.conv.23, Yolov4.conv.137, Yolov4-tiny.conv.29).
  • Eight distinct waste classes were defined, and a dataset of 3171 waste images was used for training and evaluation.
  • The Kestrel-based Search Algorithm (KSA) was employed to determine the optimal learning rate for the CNN model.

Main Results:

  • The Yolov3 model achieved the highest prediction accuracy with a mean Average Precision (mAP) of 80%, outperforming Yolov3-tiny (57%), Yolov4 (41%), and Yolov4-tiny (74%).
  • While Yolov3-tiny offered faster prediction speeds, its accuracy was significantly limited compared to Yolov3.
  • The combination of the KSA learning rate (0.0007) and the Yolov3 model proved to be the most accurate for waste object detection and classification.

Conclusions:

  • The Yolov3 model, optimized with KSA for learning rate determination, is highly effective for waste object detection and classification in smart waste bin systems.
  • Implementing an EdgeIoT-enabled system with Yolov3 can significantly enhance the efficiency of waste collection and management.
  • Nature-inspired search algorithms show considerable promise for optimizing parameters in AI models for environmental applications like waste management.