Optimized YOLOV8: An efficient underwater litter detection using deep learning
Underwater litter has been a major issue in preserving the marine ecosystem. Human waste is deposited into lakes, rivers, and seas which leads to polluted water. The underwater litter harms aquatic life and pollutes water bodies and ecosystems. Therefore, there is a need for effective and efficient...
Saved in:
Main Authors: | Faiza Rehman, Mariam Rehman, Maria Anjum, Afzaal Hussain |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-01-01
|
Series: | Ain Shams Engineering Journal |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447924006087 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Performance evaluation of hyper-parameter tuning automation in YOLOV8 and YOLO-NAS for corn leaf disease detection
by: Huzair Saputra, et al.
Published: (2025-01-01) -
Advancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring
by: Ruiqian Qin, et al.
Published: (2025-01-01) -
AquaYOLO: Enhancing YOLOv8 for Accurate Underwater Object Detection for Sonar Images
by: Yanyang Lu, et al.
Published: (2025-01-01) -
Underwater Target Detection with High Accuracy and Speed Based on YOLOv10
by: Zhengliang Hu, et al.
Published: (2025-01-01) -
ESD-YOLOv8: An Efficient Solar Cell Fault Detection Model Based on YOLOv8
by: Lingyun Zhang, et al.
Published: (2024-01-01)