Underwater Waste Detection Using Deep Learning A Performance Comparison of YOLOv7 to 10 and Faster RCNN

TL;DR

This study compares the performance of five object recognition models, finding YOLOv8 most effective for underwater waste detection with an 80.9% mAP, demonstrating its potential for environmental cleanup.

Contribution

It provides a comprehensive performance comparison of YOLOv7 to YOLOv10 and Faster R-CNN specifically for underwater waste detection, highlighting YOLOv8's superior accuracy.

Findings

YOLOv8 achieved a mean Average Precision of 80.9%.

YOLOv8's architecture includes improved anchor-free mechanisms.

YOLOv8 outperformed other models across diverse underwater conditions.

Abstract

Underwater pollution is one of today's most significant environmental concerns, with vast volumes of garbage found in seas, rivers, and landscapes around the world. Accurate detection of these waste materials is crucial for successful waste management, environmental monitoring, and mitigation strategies. In this study, we investigated the performance of five cutting-edge object recognition algorithms, namely YOLO (You Only Look Once) models, including YOLOv7, YOLOv8, YOLOv9, YOLOv10, and Faster Region-Convolutional Neural Network (R-CNN), to identify which model was most effective at recognizing materials in underwater situations. The models were thoroughly trained and tested on a large dataset containing fifteen different classes under diverse conditions, such as low visibility and variable depths. From the above-mentioned models, YOLOv8 outperformed the others, with a mean Average Precision (mAP) of 80.9%, indicating a significant performance. This increased performance is attributed to YOLOv8's architecture, which incorporates advanced features such as improved anchor-free mechanisms and self-supervised learning, allowing for more precise and efficient recognition of items in a variety of settings. These findings highlight the YOLOv8 model's potential as an effective tool in the global fight against pollution, improving both the detection capabilities and scalability of underwater cleanup operations.