AUTOMATED ELECTRONIC WASTE IDENTIFICATION MODEL USING TRANSFER LEARNING METHOD AND YOU ONLY LOOK ONCE DETECTION ALGORITHM

Abstract

The identification of waste composition based on target-detection is crucial in
promoting sustainable solid waste management. However, discrimination of
different solid waste categories in the presence of incomplete and insufficient
feature information remains a challenge in multi-target detection. The study
aims to develop an automated model for electronic waste identification by
integrating transfer learning with the YOLO (You Only Look Once) detection
algorithm using kaggle repository dataset. The Model enhanced real-time object
detection, improve classification accuracy, and support efficient e-waste sorting
and management. By utilizing deep learning techniques, the system facilitates
the rapid and accurate recognition of various electronic waste components,
contributing to sustainable waste disposal and recycling efforts. The
experimental results showed that the improved model achieved a mean average
precision (mAP) of 0.950 which reduced incidents related to inaccurate
positioning and false and missed detection. Moreover, the improved model
outperformed classical detection models and is expected to be applied to
intelligent monitoring for waste components in scenarios including
indiscriminate waste disposal and illegal dumping, providing decision support
for emergency management.