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DWSD: Dense waste segmentation dataset.

Asfak Ali¹, Suvojit Acharjee², Md Manarul Sk¹

¹Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, India.

|February 25, 2025

Summary

This summary is machine-generated.

This study introduces a new dataset to enhance automatic waste segmentation systems. The dataset aids in developing better recycling technologies by classifying 14 waste types from annotated images.

Keywords:

Classification and segmentation Computer vision Smart cities Waste management

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Area of Science:

Computer Science
Environmental Science
Data Science

Background:

Waste disposal is a significant global issue, particularly in urban areas.
Effective waste segregation is crucial for recycling and resource management.
Current manual waste segregation methods are inefficient compared to automated systems.

Purpose of the Study:

To present a novel, manually annotated dataset for improving automatic waste segmentation.
To support the development of advanced waste classification technologies.
To address the limitations of manual waste sorting in developing regions.

Main Methods:

Collected 784 images of waste from various locations around Jadavpur University.
Manually annotated images using the Labelme program, creating color annotations.
Classified waste into 14 distinct categories, including plastics, paper, glass, and metal.

Main Results:

The dataset contains 784 images with 2350 object segments.
Annotations cover 14 diverse waste categories, facilitating detailed classification.
The dataset is formatted for use in training machine learning models for waste segmentation.

Conclusions:

The developed dataset is a valuable resource for advancing automated waste segmentation.
This resource can significantly contribute to improving recycling efficiency and waste management strategies.
The dataset provides a foundation for future research in intelligent waste management systems.