基于改进YOLOv4的实时目标检测方法研究
Research on Real-time Object Detection Method Based on Improved YOLOv4
鲁健恒1
作者信息
- 1. 广州华商学院人工智能学院,广东 广州 511300
- 折叠
摘要
为提升实时目标检测的准确性和稳健性,该文采用增强特征融合技术、网络架构技术、损失函数技术等对YOLOv4算法进行优化.结果表明,改良后的YOLOv4算法在多变环境下对小型目标检测表现出色,展现了其实用性和稳定性,为广泛应用奠定了坚实基础.
Abstract
To enhance the accuracy and robustness of real-time object detection,this paper optimizes the YOLOv4 algorithm by employing enhanced feature fusion technology,network architecture technology,loss function technology,and other strategies.The results demonstrate that the improved YOLOv4 algorithm exhibits excellent performance in detecting small objects in diverse environments,showcasing its practicality and stability,and laying a solid foundation for its widespread application.
关键词
实时目标检测/YOLOv4/特征融合/GIoU损失函数Key words
real-time object detection/YOLOv4/feature fusion/GIoU loss function引用本文复制引用
出版年
2024
NETL
NSTL
万方数据