YOLOv8 vs. YOLOv9: Evaluating Object Detection Algorithms for Marine Waste Recognition

Marine debris, the majority of which is composed of plastic (61 % to 87 %), is a significant environmental issue facing the world. Between 4.8 million and 12.7 million metric tons of plastic are thought to have entered the ocean in 2010 alone, increasing the estimated total amount of particles in th...

Full description

Saved in:
Bibliographic Details
Main Authors: Wajdi, Achmad Zahir, Wibowo, Prasetyo, Sasaki, Shiori
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Context modeling
Marine Waste
Measurement
Monitoring
Neural Net-work
Object Detection
Oceans
Plastics
Production
TACO Dataset
Tuning
Waste management
YOLO
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Marine debris, the majority of which is composed of plastic (61 % to 87 %), is a significant environmental issue facing the world. Between 4.8 million and 12.7 million metric tons of plastic are thought to have entered the ocean in 2010 alone, increasing the estimated total amount of particles in the water to at least 5 trillion. Global plastic production, which reached 348 million tons in 2017, is expected to double in the next 20 years. To overcome this challenge, effective waste detection methods are needed, one of which is by utilizing computer vision, specifically the You Only Look Once (YOLO) algorithm. YOLOv9, as the latest version, offers improved object detection performance compared to its predecessor, YOLOv8. This research aims to evaluate the performance and efficiency of YOLOv9 and YOLOv8 in detecting marine waste. Using the Trash Annotations in Context dataset (TACO), We train both models and conduct tests to evaluate their accuracy and efficiency. Experimental results show that YOLOv9 has higher precision at 45% but lower recall at 6%, compared to YOLOv8, which has a precision of 33 % and a recall of 15 %. Overall, YOLOv8 shows better performance with higher mAP50 at 13% and mAP50-95 values at 10%, compared to YOLOv9, which has mAP50 at 4% and mAP50-95 values at 3%.
ISSN:2159-1423
DOI:10.1109/ISCT62336.2024.10791223