Enhancing real-time instance segmentation for plant disease detection with improved YOLOv8-Seg algorithm

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Mohamed Ammar
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引用次数: 0

Abstract

With widespread uses in areas as diverse as traffic analysis and medical imaging, picture segmentation is a basic problem in computer vision. Instance segmentation, which combines object recognition with segmentation, is a powerful tool for item identification and exact delineation. Using the Tomato Leaf disease dataset as an example, this research delves into the topic of segmentation training by capitalizing on the simplicity of enhanced YOLOv8-Seg models. Tomato leaf disease are the focus of this instance-segmentation dataset, which seeks to resolve the pressing problem of agricultural difficulties. One instance segmentation networks, YOLOv8n-Seg is presented and compared in this article for the purpose of Tomato leaf disease identification. The models are tested in difficult situations to see how well they can detect and separate garbage occurrences. Results show that enhanced YOLOv8-Seg is useful for agriculture by accurately segmenting instances of tomato leaf disease detection.
利用改进的 YOLOv8-Seg 算法提高植物病害检测的实时实例分割能力
图片分割广泛应用于交通分析和医学成像等不同领域,是计算机视觉领域的一个基本问题。将物体识别与分割相结合的实例分割是物品识别和精确划分的有力工具。本研究以番茄叶疾病数据集为例,利用增强型 YOLOv8-Seg 模型的简易性,深入探讨了分割训练的主题。番茄叶病是该实例分割数据集的重点,旨在解决农业难题这一紧迫问题。本文介绍了一个实例分割网络 YOLOv8n-Seg,并对其进行了比较,以识别番茄叶病。这些模型在困难的情况下进行了测试,以了解其检测和分离垃圾发生的能力。结果表明,增强型 YOLOv8-Seg 能准确分割番茄叶病检测实例,对农业很有帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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