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V. Conclusion, Acknowledgment, and References
V. CONCLUSION
The outcomes and results derived from the evaluation of YOLOv5 and YOLOv8 in object detection present an intriguing and subtle picture. The analysis of SSIM in our study further evaluates the context of object detection performance between YOLOv5 and YOLOv8. The high SSIM values observed across various image concentrations emphasize the importance of maintaining consistent image quality and structural integrity in training datasets. For example, the precision in detecting Artemia and cyst using YOLOv5 suggests that its performance benefits significantly from high-quality, structurally similar images. One the other hand, the slight overall decrease in SSIM values with increased concentration levels indicates the subtle challenges YOLOv8 faces in maintaining detection accuracy across varying image qualities and structural similarities. While both models exhibit strengths and capabilities, the findings suggest that YOLOv5 may excel over YOLOv8 in certain scenarios. However, it is equally apparent that the performance of these models can be context-dependent and class-specific.
One of the noteworthy observations from the evaluation is that YOLOv5 demonstrated superior performance in detecting Artemia and cyst in several instances, showcasing its potential in precision-driven detection tasks. Its agility and accuracy in these areas are promising, particularly for applications where accurate object recognition is paramount.
However, it is equally important to acknowledge that YOLOv5 exhibited limitations, particularly in the detection of less-represented classes, such as excrement. In cases where there are limited instances of a class within the labeled dataset, YOLOv5 appeared to struggle in accurately detecting those class objects.
On the other hand, YOLOv8 demonstrated robustness in detecting objects across a wider range of classes, even in scenarios with limited instances of a class. This observation implies that YOLOv8 may offer greater versatility and adaptability in certain detection tasks, even when training data is scarce for specific classes.
ACKNOWLEDGMENT
This work is supported by the National Science Foundation (award number: 2038484, year: 2020).
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Authors:
(1) Mahmudul Islam, Masum School of Computing and Information Sciences, Florida International University Miami, USA ([email protected]);
(2) Arif Sarwat, Department of Electrical and Computer Engineering, Florida International University Miami, USA ([email protected]);
(3) Hugo Riggs,Department of Electrical and Computer Engineering, Florida International University Miami, USA ([email protected]);
(4) Alicia Boymelgreen, Department of Mechanical and Materials Engineering, Florida International University Miami, USA ([email protected]);
(5) Preyojon Dey, Department of Mechanical and Materials Engineering, Florida International University Miami, USA ([email protected]).
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