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基于YOLOv8的目标检测算法改进研究

Research on the Improvement of Target Detection Algorithm Based on YOLOv8

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现有的目标检测网络往往存在结构复杂、参数量巨大的问题.因此,研究结构简单、参数量少的高精度目标检测算法,其重要性和价值不言而喻.YOLO系列算法作为深度学习时代具有代表性的单阶段目标检测算法,具有准确、高效和易于部署等特点,为目标检测提供了非常好的理论及技术基础.因此,文章在YOLOv8 的基础上对其卷积网络进行改进,实验结果证明,改进算法成功提升了检测精度.
The existing target detection network often has the problems of complex structure and huge parameters.Therefore,it is difficult to study the high-precision target detection algorithm with simple structure and a few parameters.As a representative single-stage target detection algorithm in the era of Deep Learning,YOLO series algorithms have the characteristics of accuracy,high efficiency and easy deployment,which provides a very good theoretical and technical basis for target detection.Therefore,this paper improves its convolutional network based on YOLOv8 and successfully improves the detection accuracy.

target detectionhigh accuracyYOLOv8convolutional network

肖富坤

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沈阳航空航天大学,辽宁 沈阳 110136

目标检测 高精度 YOLOv8 卷积网络

2024

现代信息科技
广东省电子学会

现代信息科技

ISSN:2096-4706
年,卷(期):2024.8(18)