Plastic Waste Detection Using Deep Learning: Insights from the WaDaBa Dataset

2025-02-19T03:33:57+00:00

With the increasing use of plastic, the challenges associated with managing plastic waste have become more difficult, emphasizing the need for effective classification and recycling solutions. This study explored the potential of deep learning, focusing on convolutional neural networks (CNNs) and object detection models like YOLO to tackle this issue using the WaDaBa dataset. The results indicated that YOLO-11m achieved the highest accuracy (98.03%) and mAP50 (0.990), while YOLO-11n performed similarly but achieved the highest mAP50 (0.992). Lightweight models like YOLO-10n trained faster but had lower accuracy, whereas MobileNetV2 demonstrated impressive performance (97.12% accuracy) but fell short in object detection. YOLO-11n had the fastest inference time (0.2720s), making it ideal for real-time detection, while YOLO-10m was the slowest (5.9416s). Among CNNs, ResNet50 had the best inference time (1.3260s), whereas MobileNetV2 was the slowest (1.4991s). These findings suggested that by balancing accuracy and computational efficiency, these models could contribute to scalable waste management solutions. The study recommended increasing the dataset size for better generalization, enhancing augmentation techniques, and developing real-time solutions.

The Relationship between Households Average Formal Education Levels and Sanitation Practices in Mojo, Surabaya, Indonesia

2025-03-02T01:29:41+00:00

This study explored the relationship between households’ average formal education levels and sanitation practices. Although formal education was intended to prepare individuals for personal and professional life situations, local habits and cultural practices could sometimes be more influential than educational background, as evidenced by urinary habits practiced in the country. These habits played a crucial role in determining whether urine was disposed of in the toilet, processed in a septic tank, or directly entered the drainage system when spilled on the bathroom floor. In this study, the definition of sanitation differed from that previously outlined by the Sustainable Development Goals (SDGs). The SDGs defined sanitation based on the percentage of households that used safely managed services, including handwashing facilities. This study, however, focused on excreta disposal, desludging intervals, septic tank types, and urinary habits, such as whether urine was disposed of on the bathroom floor or in the toilet. These factors were chosen for their ability to accurately reflect the actual conditions observed in the study area. A survey was conducted among 100 households, and data were analyzed using Analysis of Variance (ANOVA). The results revealed no relationship between households’ average formal education levels and sanitation practices. This analysis suggested that other factors, such as cultural beliefs and environmental habits, may have influenced sanitation practices.

Industrial and Domestic Waste Management

Plastic Waste Detection Using Deep Learning: Insights from the WaDaBa Dataset

The Relationship between Households Average Formal Education Levels and Sanitation Practices in Mojo, Surabaya, Indonesia