Comparison of YOLOv5 and YOLOv8 Neural Networks for Grape Recognition

XVII International Conference on Systems, Automatic Control and Measurements, SAUM 2024 (pp. 173-176)

АУТОР(И) / AUTHOR(S): Jelena Dimitrijević , Miroslav Milovanović , Jianxun Cui , Milan Banić

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DOI:  10.46793/SAUM24.173D

САЖЕТАК / ABSTRACT:

This paper presents the results of a comparative analysis of two pre-trained deep learning models, YOLOv5 and YOLOv8, for object detection in images. Considering the current research focus and the development of autonomous agriculture, the models were trained to recognize grape instances using images from the existing WGISD dataset. The final evaluation of the models, following the comparative analysis, is based on performance metrics (precision, recall, F1 score, and AP) and results obtained from testing the models on an unseen dataset. The YOLOv5 model recorded an almost negligible number of false positive predictions (7 instances); however, this came at the cost of failing to detect a significant number of grape instances (97 instances). In contrast, the YOLOv8 model detected more grape instances (170 instances) but with a higher number of false positive predictions (18 instances). In terms of performance evaluation metrics, YOLOv8 demonstrated better overall results.

КЉУЧНЕ РЕЧИ / KEYWORDS:

deep learning, YOLOv5, YOLOv8, grape recognition, object detection

ПРОЈЕКАТ/ ACKNOWLEDGEMENT:

This work was supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia [grant number 451-03-66/2024-03/200102].

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