Introduction to Garbage Classification Using Deep Learning

Garbage classification, also known as waste sorting or trash categorization, is a critical application in environmental management, recycling, and smart city initiatives. Deep learning (DL) techniques, particularly convolutional neural networks (CNNs), are widely used for this task because they excel at image recognition and can automatically learn features from waste images to classify them into categories like plastic, paper, metal, organic, glass, and more. This automation helps reduce manual labor, improve recycling efficiency, and minimize environmental pollution. Recent advancements integrate DL with IoT for real-time systems, achieving accuracies over 90% in many cases.

DL models for garbage classification typically involve:

• Image-based classification: Treating the problem as multi-class image classification.
• Detection and segmentation: Using object detection models (e.g., YOLO) for identifying waste in complex scenes.
• Transfer learning: Fine-tuning pre-trained models like ResNet or VGG on waste-specific datasets to overcome limited data.

High accuracies are reported, such as 98.97% using fused architectures like SwinConvNeXt on public datasets, or 97.7% with YOLOv8 for real-time classification