利用 YOLOv5 模型序列检测枣树上的害虫 Metcalfa pruinosa(半翅目:扁科

Atilla Erdinç, Hilal Erdoğan
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引用次数: 0

摘要

本研究旨在使用 YOLOv5 算法的 v5s、v5m 和 v5l 模型检测在枣类植物上观察到的害虫 Metcalfa pruinosa 的成虫。研究中观察到的性能指标包括 box_loss、obj_loss、精确度、召回率、mAP_0.5 和 mAP_0.5:0.95。在 YOLOv5s 模型中,box_loss 和 obj_loss 的性能指标最高,分别为 0.02858 和 0.0055256。在 YOLOv5m 模型中,召回率性能指标最高,值为 0.98127。在 YOLOv5l 模型中,精确度、mAP_0.5 和 mAP_0.5:0.95 性能指标最高,分别为 0.98122、0.99500 和 0.67864。因此,与其他模型相比,YOLOv5l 模型表现出更高的精度。我们认为,YOLOv5l 模型足以用于检测 Metcalfa pruinosa 害虫。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of the Metcalfa pruinosa (Hemiptera: Flatidae) pest on the Jujube plant (Ziziphus jujuba) using a sequence of YOLOv5 models
This study aimed to detect the adult of the pest Metcalfa pruinosa observed on jujube plants using the YOLOv5 algorithm's v5s, v5m, and v5l models. Performance metrics, including box_loss, obj_loss, precision, recall, mAP_0.5, and mAP_0.5:0.95, were observed in the research. In the YOLOv5s model, the box_loss and obj_loss performance metrics were found to be the highest, with values of 0.02858 and 0.0055256, respectively. In the YOLOv5m model, the recall performance metric was identified as the highest, with a value of 0.98127. In the YOLOv5l model, precision, mAP_0.5, and mAP_0.5:0.95 performance metrics were identified as the highest, with values of 0.98122, 0.99500, and 0.67864, respectively. Consequently, the YOLOv5l model exhibits higher precision compared to others. It is believed that the YOLOv5l model is sufficient for the detection of the Metcalfa pruinosa pest.
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