A comprehensive approach for waste management with GAN-augmented classification.

Area of Science:

• Computer Vision
• Machine Learning
• Environmental Science

Background:

• Data augmentation is crucial for machine learning model performance, especially in image classification.
• Classifying waste objects is a key application for improving waste management and sustainability.

Purpose of the Study:

• To augment waste object images using Generative Adversarial Networks (GANs).
• To enhance waste object detection and classification accuracy using ensemble learning.
• To investigate the effectiveness of GAN-based augmentation with ensemble models for real-world applications.

Main Methods:

• Utilized Deep Convolutional GAN (DCGAN) for realistic image generation and variability.
• Employed ensemble learning with DenseNet121, ConvNeXt, and ResNet101 for object detection and classification.
• Integrated GAN-based data augmentation with ensemble classification models.

Main Results:

• Achieved a high classification accuracy of 99.80% using ensemble learning.
• Demonstrated the effectiveness of GANs in generating diverse and realistic waste object images.
• Validated the novel approach for optimizing waste object identification.

Conclusions:

• GAN-based data augmentation combined with ensemble models significantly improves waste object classification accuracy.
• This approach offers valuable insights for optimizing waste management through advanced AI techniques.
• Future research will explore advanced GAN architectures and multimodal data for further performance enhancement.