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Indonesia, as a maritime country, has a significant portion of its territorycovered by water. Ineffective waste management has resulted in a considerableamount of trash in Indonesian waters, leading to various issues. Thedevelopment of an automated trash-collecting robot can be a solution to addressthis problem. The robot requires a system capable of detecting objects inmotion, such as in videos. However, using naive object detection methods invideos has limitations, particularly when image focus is reduced and the targetobject is obstructed by other objects. This paper's contribution provides anexplanation of the methods that can be applied to perform video objectdetection in an automated trash-collecting robot. The study utilizes the YOLOv5model and the Robust & Efficient Post Processing (REPP) method, along withtubelet-level bounding box linking on the FloW and Roboflow datasets. Thecombination of these methods enhances the performance of naive object detectionfrom YOLOv5 by considering the detection results in adjacent frames. Theresults show that the post-processing stage and tubelet-level bounding boxlinking can improve the quality of detection, achieving approximately 3% betterperformance compared to YOLOv5 alone. The use of these methods has thepotential to detect surface and underwater trash and can be applied to areal-time image-based trash-collecting robot. Implementing this system isexpected to mitigate the damage caused by trash in the past and improveIndonesia's waste management system in the future.

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