Deep Learning Model for Enhancing Automated Recycling Machine with Incentive Mechanisms

Automated Recycling Machine (ARM) can be defined as an interactive tool to flourish recycling culture among community by providing incentive to the user that deposit the recyclable items. To enable this, the machine crucially needs a material validation module to identify the deposited recyclable items. Utilizing combination of sensors for such purpose is a tedious task and hence vision-based YOLO detection framework is proposed to identify three types of recyclable material which are aluminum can, PET bottle and tetra-pak Initially, the 14883 training samples and 937 validation samples were fed to the various YOLO variants for investigating an optimal model that can yield high accuracy and suitable for CPU usage during inference stage. Next the user interface is constructed to effectively communicate with the user when operating the ARM with easy-to-use graphical instruction. Eventually, the ARM body was designed and developed with durable material for usage in indoor and outdoor conditions. From series of experiments, it can be concluded that, the YOLOv8-m detection model well suit for the ARM material identification usage with 0.949 mAP@0.5:0.95 score and 0.997 F1 score. Field testing showed that the ARM effectively encourages recycling, evidenced by the significant number of recyclable items collected.

@article{Tomari2024,
  title     = {Deep Learning Model for Enhancing Automated Recycling Machine with Incentive Mechanisms},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Razali Tomari and Aeslina Abdul Kadir and Wan Nurshazwani Wan Zakaria and Dipankar Das and Muhamad Bakhtiar Azni},
  doi       = {10.14569/IJACSA.2024.0150880},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150880}
}