Low resource deep learning to detect waste intensity in the river flow

Ferdinandus Fidel Putra, Yulius Denny Prabowo

Abstract


Waste has become a significant problem in Indonesia, especially in the capital city of Jakarta due to many disasters caused by it. The one cause of flooding is the blockage of river flow by waste. The monitoring of litter is essential to find out the waste intensity in the river. The research was formed which aims to produce an application that can detect, track, and calculate river waste using YOLO v3 algorithm. This research was done in order to simplify the process of monitoring waste in the river and can calculate waste using video. This research uses 340 images directly from photos and videos, captured by researchers—detection of waste processed frame by frame by changing video into several structures. From the acquired result from the experiment, it's proven that YOLO v3 can be used for detection and counting waste recorded on video. The result of this research is an application that can detect waste and it is able to detect said objects with 98,74% of confidence from case video.

Keywords


Convolutional neural networks; Video processing; Waste detection; YOLO V3;



DOI: https://doi.org/10.11591/eei.v10i4.3062

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Bulletin of EEI Stats