YOLOv5 vs. YOLOv8 in Marine Fisheries: Balancing Class Detection and Instance Count

TL;DR

This study compares YOLOv5 and YOLOv8 for detecting marine objects, revealing YOLOv5's superior performance on certain classes and YOLOv8's greater versatility in challenging detection scenarios.

Contribution

It provides a detailed comparison of YOLOv5 and YOLOv8 in marine object detection, highlighting their strengths and limitations in different detection contexts.

Findings

YOLOv5 outperforms YOLOv8 in detecting Artemia and cysts.

YOLOv8 demonstrates greater versatility in challenging detection scenarios.

YOLOv5 struggles with detecting excrement in marine environments.

Abstract

This paper presents a comparative study of object detection using YOLOv5 and YOLOv8 for three distinct classes: artemia, cyst, and excrement. In this comparative study, we analyze the performance of these models in terms of accuracy, precision, recall, etc. where YOLOv5 often performed better in detecting Artemia and cysts with excellent precision and accuracy. However, when it came to detecting excrement, YOLOv5 faced notable challenges and limitations. This suggests that YOLOv8 offers greater versatility and adaptability in detection tasks while YOLOv5 may struggle in difficult situations and may need further fine-tuning or specialized training to enhance its performance. The results show insights into the suitability of YOLOv5 and YOLOv8 for detecting objects in challenging marine environments, with implications for applications such as ecological research.