  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.
