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Aggregates Classification

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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...

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Related Experiment Video

Updated: Jun 22, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Enhancing waste classification accuracy with Channel and Spatial Attention-Based Multiblock Convolutional Network.

Jithina Jose¹, Suja Cherukullapurath Mana², Keerthi Samhitha Babu³

¹School of Computer Engineering, MIT Academy of Engineering, 412105, Alandi, Pune, Maharashtra, India.

Environmental Monitoring and Assessment

|January 25, 2025

Summary

This summary is machine-generated.

This study introduces a new deep learning model for municipal waste classification, achieving 98.73% accuracy. The Channel and Spatial Attention-Based Multiblock Convolutional Network improves recycling and waste management efficiency.

Keywords:

Augmentation Channel and spatial attention Convolutional layer Feature extraction Image patch Municipal waste

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Last Updated: Jun 22, 2026

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Area of Science:

Computer Science
Environmental Science
Artificial Intelligence

Background:

Effective municipal waste classification is crucial for recycling and waste management.
Current methods struggle with computational complexity, time consumption, and visual variability of waste.

Purpose of the Study:

To propose a novel Channel and Spatial Attention-Based Multiblock Convolutional Network for accurate municipal waste classification.
To enhance feature learning and classification accuracy using an attention mechanism.

Main Methods:

Utilized data augmentation to increase image dataset size and diversity.
Applied data preprocessing including normalization, resizing, and image patching.
Employed a Channel and Spatial Attention-Based Multiblock Convolutional Network for feature extraction and waste classification.

Main Results:

Achieved a high accuracy of 98.73% in municipal waste image classification.
Reported a low mean absolute error of 0.048 and root mean square error of 0.087.
Demonstrated superior performance compared to existing waste classification strategies.

Conclusions:

The proposed network offers a more accurate and reliable solution for municipal waste classification.
The framework is well-suited for real-time applications in waste management.
The attention mechanism effectively enhances feature learning for improved classification.