2022 Volume 3 Issue 3 Pages 1-9
The unlawful disposal of aluminum and plastic materials, such as empty cans and plastic bottles, has been a serious social issue. We are all concerned that this issue could harm ecosystems and increase pollution. As a result, social volunteer projects have started picking up trash to address this issue. These activities, however, take a lot of time and cost. Therefore, in this work, we propose the automation of trash pickup by robots as a solution to this problem. Trash pickup robots must perform several functions, including moving autonomously and detecting and collecting unlawfully dumped materials. This study develops a technology to detect illegally dumped objects from videos captured by a robot. Furthermore, this research suggests a faster object detection technique that enhances the YOLO low memory, quick method. Additionally, we developed an annotated image dataset with three new types of trash (plastic bottles, cans, and cardboard). We used this data in validation trials to compare the performance of the proposed method with that of existing methods. The experimental results demonstrated that our model could detect the three types of trash faster than the selected models and enhanced the processing speed. Finally, a comparison was made for frames per second on GPUs and edge devices to validate whether all models could detect trash in realtime.