YOLOv5 vs. YOLOv8 in Marine Fisheries: Balancing Class Detection and Instance Count
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909189012455424 |
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| author | Masum, Mahmudul Islam Sarwat, Arif Riggs, Hugo Boymelgreen, Alicia Dey, Preyojon |
| author_facet | Masum, Mahmudul Islam Sarwat, Arif Riggs, Hugo Boymelgreen, Alicia Dey, Preyojon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02312 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | YOLOv5 vs. YOLOv8 in Marine Fisheries: Balancing Class Detection and Instance Count Masum, Mahmudul Islam Sarwat, Arif Riggs, Hugo Boymelgreen, Alicia Dey, Preyojon Computer Vision and Pattern Recognition Image and Video Processing 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. |
| title | YOLOv5 vs. YOLOv8 in Marine Fisheries: Balancing Class Detection and Instance Count |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2405.02312 |