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.

印度尼西亚是一个海洋国家，由于无效的垃圾管理，海洋中积聚了大量的垃圾，影响严重。本研究提出了一种自动垃圾清理机器人的视频物体检测方法，利用 YOLOv5 模型和 Robust & Efficient Post Processing (REPP) 方法，通过考虑邻近帧的检测结果，提高了检测性能，相较于仅使用 YOLOv5，性能提高了约 3%。该方法有潜力用于实时基于图像的垃圾清理机器人，有望改善印度尼西亚的垃圾处理系统，减轻过去垃圾造成的损害。

视频中表面和水中垃圾的检测：鲁棒高效的后处理和管道级绑定框链接

Deteksi Sampah di Permukaan dan Dalam Perairan pada Objek Video dengan  Metode Robust and Efficient Post-Processing dan Tubelet-Level Bounding Box  Linking