TrashNet: An object detection model that classifies images of trash in real-time

Abstract

Global warming and pollution are huge problems today. One of the main factors behind these two problems is plastic pollution. As plastic takes millennia to decompose, an estimated 270,000 tons of plastic are floating around our oceans, which is too much to be considered “safe.” This problem is mainly caused by the fact that people improperly dispose of plastic, whether that be through littering or putting it into the trash instead of a recycling bin. To better identify and correct plastic that was inappropriately disposed of, a deep-learning model (YOLOv5) that uses object detection and classification was implemented to detect which bin someone’s trash should go in. The YOLOv5 uses the PyTorch framework for the object detection model. Using this model helped solve this problem, as a custom object-detection model would have needed to be developed, which would not have been efficient. The model was tested by trying to run the model on pieces of trash placed on a tabletop and analyzing the code output on which trash bin the waste will have to be thrown away. After conducting multiple tests, the model exhibited a commendable accuracy rate of 90%, which is noteworthy given the substantial amount of data leveraged. To further improve its efficacy and real-world value, future research could explore augmenting the training data, refining the object detection model for greater precision, and expanding the dataset to encompass a broader range of use cases.