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

Jithina Jose ¹, Suja Cherukullapurath Mana ², Keerthi Samhitha Babu ³

⁰School of Computer Engineering, MIT Academy of Engineering, 412105, Alandi, Pune, Maharashtra, India.

Environmental Monitoring and Assessment +

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January 25, 2025

View abstract on PubMed

Summary

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.

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.

Keywords:

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