The illegal dumping of aluminum and plastic into cities and marine areas leads to negative impacts on the ecosystem and contributes to increased environmental pollution. Although volunteer
trash
pickup activities have increased in recent years, they require significant effort, time, and money. Therefore, we propose automated
trash
pickup robot, which incorporates autonomous movement and
trash
pickup arms. Although these functions have been actively developed, relatively little research has focused on
trash
detection. As such, we have developed a
trash
detection function by using deep learning models to improve the accuracy. First, we created a new
trash
dataset that classifies four types of
trash
with high illegal dumping volumes (cans, plastic bottles, cardboard, and cigarette butts). Next, we developed a new you only look once (YOLO)-based model with low parameters and computations. We trained the model on a created dataset and a dataset consisting of marine
trash
created during previous research. In consequence, the proposed models achieve the same detection accuracy as the existing models on both datasets, with fewer parameters and computations. Furthermore, the proposed models accelerate the edge device’s frame rate.
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