Development of Machine Vision System for Riverine Debris Counting
In Malaysia, about 80% of freshwater sources come from rivers, but 44% of rivers are polluted. One of the river cleaning efforts is via Ocean Cleanup's Interceptor river cleaning machine. The efficiency depends on its location at the river, which is highly dependent on debris count along the ri...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
2022
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/98748/ http://dx.doi.org/10.1109/ICRAIE52900.2021.9704016 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In Malaysia, about 80% of freshwater sources come from rivers, but 44% of rivers are polluted. One of the river cleaning efforts is via Ocean Cleanup's Interceptor river cleaning machine. The efficiency depends on its location at the river, which is highly dependent on debris count along the river currently counted by human manual operators. Unfortunately, the process is not continuous and can only be done few hours in daylight. This project proposed to replace manual counting with a continuous automated debris counting system using computer vision. The system consists of a camera connected to a computer with algorithms that process the river live video feed and automatically detect and count riverine debris. The system was trained using three datasets over two You Only Look Once (YOLOv4) configurations producing six YOLOv4 models. The system was tested on a 5-minutes video of a flowing water source with floating debris, and the system's best performance, to match human counting, was by 110% or 10% better than human counting. This count may assist decision-making in locating the river cleaning interceptor and increase the efficiency of river cleaning activities. |
|---|