YOLOv5 vs YOLOv8 in Marine Fisheries: Balancing Class Detection and Instance Count
<|reference_start|>YOLOv5 vs YOLOv8 in Marine Fisheries: Balancing Class Detection and Instance Count: 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.<|reference_end|>
@article{masum2024yolov5,
    title={YOLOv5 vs. YOLOv8 in Marine Fisheries: Balancing Class Detection and
  Instance Count},
    author={Mahmudul Islam Masum, Arif Sarwat, Hugo Riggs, Alicia Boymelgreen,
  Preyojon Dey},
    journal={arXiv preprint arXiv:2405.02312},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02312},
    primaryClass={cs.CV eess.IV}
}