Real-Time Household Waste Detection and Classification for Sustainable Recycling: A Deep Learning Approach

As global waste production continues to rise, improper handling of household waste significantly contributes to environmental pollution and resource depletion. Inefficient sorting at the household level leads to the contamination of recyclables, reducing recycling efficiency and increasing landfill waste. Effective waste sorting is essential for conserving manual labor, protecting the environment, and ensuring sustainable development for human progress. Recently, advancements in deep learning and computer vision have offered a promising pathway to improve the sorting process, though significant developmental steps are still required. Enhancing the efficiency of automated waste detection and classification through computer vision could bring substantial societal and environmental benefits. However, classifying and identifying waste materials presents challenges due to the complex and diverse nature of waste, coupled with the limited availability of data on waste management. This paper presents a real-time waste detection and classification system based on the YOLOv8 deep learning model, designed to enhance waste sorting processes at the household level. The proposed system detects and classifies a diverse range of household waste items. Experiments were conducted on a custom waste dataset comprising 3775 images across 17 types of common household waste. The one-stage YOLOv8 model demonstrated superior performance, outperforming traditional two-stage detectors. To improve the accuracy and robustness of the original YOLOv8, five data augmentation techniques and two attention mechanisms were incorporated. Notably, the enhanced YOLOv8-CBAM model achieved a mean average precision (mAP) of 89.5%, a significant improvement with a 4.2% increase over the baseline model. The methodology and improvements applied provide a more efficient and effective AI framework for real-time applications in smart bins, robotic waste pickers, and large-scale recycling systems.