  Indonesia, as a maritime country, has a significant portion of its territory
covered by water. Ineffective waste management has resulted in a considerable
amount of trash in Indonesian waters, leading to various issues. The
development of an automated trash-collecting robot can be a solution to address
this problem. The robot requires a system capable of detecting objects in
motion, such as in videos. However, using naive object detection methods in
videos has limitations, particularly when image focus is reduced and the target
object is obstructed by other objects. This paper's contribution provides an
explanation of the methods that can be applied to perform video object
detection in an automated trash-collecting robot. The study utilizes the YOLOv5
model and the Robust & Efficient Post Processing (REPP) method, along with
tubelet-level bounding box linking on the FloW and Roboflow datasets. The
combination of these methods enhances the performance of naive object detection
from YOLOv5 by considering the detection results in adjacent frames. The
results show that the post-processing stage and tubelet-level bounding box
linking can improve the quality of detection, achieving approximately 3% better
performance compared to YOLOv5 alone. The use of these methods has the
potential to detect surface and underwater trash and can be applied to a
real-time image-based trash-collecting robot. Implementing this system is
expected to mitigate the damage caused by trash in the past and improve
Indonesia's waste management system in the future.
