基于改进YOLOv5s算法和自适应决策的野猪检测方法
Wild Boar Detection Method Based on Improved YOLOv5s Algorithm and Adaptive Decision Making
陈明 1王善勤1
作者信息
- 1. 滁州职业技术学院,安徽滁州,239000
- 折叠
摘要
针对当前野猪目标检测方法存在检测精度低、复杂背景下算法失效等不足,提出一种基于改进YOLOv5s算法与自适应决策的野猪目标检测方法.实验结果表明,所提出的方法目标检测精度高,具备复杂背景下目标检测能力,且在多个评价指标上均高于SE-ResNeXt、Faster R-CNN、SSD、YOLOv5s基准算法四种方法,为野猪目标检测提供了一种新手段.
Abstract
Aiming at the shortcomings of current wild boar target detection methods,such as low detection accuracy and algorithm failure in complex background,a wild boar target detection method based on improved YOLOv5s algorithm and adaptive decision making is proposed.Experimental results show that the proposed method has high target detection accuracy and target detection capability under complex background,and is superior to the four benchmark algorithms SE-ResNeXt,Faster R-CNN,SSD and YOLOv5s on multiple evaluation indexes,providing a new method for wild boar target detection.
关键词
目标检测/注意力机制/特征增强/自适应决策Key words
target detection/attention mechanism/feature enhancement/adaptive decision引用本文复制引用
出版年
2024
NETL
NSTL
万方数据