Multiple object recognition system for lake using the yolov8 technique
This research tackles the challenges of underwater photography in lakes, concentrating on developing and evaluating a multiple object detection system through the advanced You Only Look Once Version 8 (YOLOv8) architecture. The inherent limited visibility in underwater environments poses difficu...
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| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2024
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/12335/1/P17271_98f7baeab787ca534f2823399c68795d.pdf%204.pdf http://eprints.uthm.edu.my/12335/ https://doi.org/10.30880/eeee.2024.05.01.011 |
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| Summary: | This research tackles the challenges of underwater photography in
lakes, concentrating on developing and evaluating a multiple object
detection system through the advanced You Only Look Once Version 8
(YOLOv8) architecture. The inherent limited visibility in underwater
environments poses difficulties in accurately capturing object shapes
and colors, crucial for applications like underwater robots engaged in
search missions. Leveraging Python and Google Colaboratory, the
project implements YOLOv8 for multiple object detection using a
dataset of 1116 lake underwater images, processed with LabelImg for
object recognition and dataset development. The publicly accessible
dataset at http://tinyurl.com/32z25b serves as a valuable resource.
YOLOv8 consistently demonstrates exceptional performance in lake
environments, achieving an impressive mean Average Precision 50-95
(mAP 50-95) of 95.5% for single-object detection in both training and
validation sets. Despite a gradual decrease to 73.8% for 5 objects in
more complex scenes, the model maintains a robust overall average of
87.42% in the test set. These findings offer valuable insights for
informed decisions when deploying YOLOv8 across diverse underwater
settings, particularly in lakes |
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