Automated Sorting: Mapping of selective collection disposal objects using artificial intelligence
DOI:
https://doi.org/10.5380/atoz.v12i0.84456Keywords:
Artificial Intelligence, Recyclable garbage, YOLOv5, Object detection.Abstract
Introduction:Artificial intelligence, especially in the field of computer vision, has emerged as a powerful tool for various applications, including object classification. In this study, we developed an applied research that utilizes artificial intelligence to detect and classify discarded objects as waste into two main categories: paper and metal. Method:The research was based on a database containing approximately 897 images of discarded objects, with 448 images of paper and 449 images of metal. We used the YOLOv5 (you only look once) model to train and test the object detection and classification. YOLOv5 is known for providing promising results in this type of task. Results:The obtained results demonstrated that the YOLOv5 model exhibited satisfactory performance in detecting and classifying the discarded objects. The achieved mean average precision was 0.88. Conclusion:The study shows that the use of artificial intelligence, through the YOLOv5 model, is effective in detecting and classifying discarded objects into recycling categories, such as paper and metal. This approach can significantly contribute to improving the selective collection process and waste management, promoting more sustainable and environmentally-conscious practices.
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