现代计算机2024,Vol.30Issue(11) :9-15,22.DOI:10.3969/j.issn.1007-1423.2024.11.002

基于YOLOv5-PNCM的飞鸟目标检测算法研究

Research on target detection algorithm of flying birds based on YOLOv5-PNCM

李耀
现代计算机2024,Vol.30Issue(11) :9-15,22.DOI:10.3969/j.issn.1007-1423.2024.11.002

基于YOLOv5-PNCM的飞鸟目标检测算法研究

Research on target detection algorithm of flying birds based on YOLOv5-PNCM

李耀1
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作者信息

  • 1. 长沙理工大学物理与电子科学学院,长沙 410114
  • 折叠

摘要

针对小目标飞鸟检测存在的检测精度低、漏检率高等问题,提出了基于YOLOv5的小目标飞鸟的实时检测算法.首先,在YOLOv5原有的检测层上添加了一层小目标检测头;其次,采用CARAFE上采样算子改进了上采样方法,引用NWD度量代替IoU,有效降低了小目标位置偏差的敏感性;最后,使用M-CBAM注意力模块.改进后的算法在自制鸟类数据集上平均精度为77.3%,检测速度达到78FPS,与改进前相比,检测精度提升了9.1%,检测速度提升了23.8%.

Abstract

Aiming at the problems of low detection accuracy and high leakage rate of small target bird detection,a real-time detection algorithm for small target bird detection based on YOLOv5 is proposed.Firstly,a layer of small target detection head is added to the original detection layer of YOLOv5,secondly,the CARAFE up-sampling operator is used to improve the up-sampling method;the NWD metric is quoted instead of the IoU,which effectively reduces the sensitivity of the positional deviation of small targets.Finally,the M-CBAM attention module is used.The improved algorithm achieves an average accuracy of 77.3%and a de-tection speed of 78 FPS on the homemade bird dataset,which is a 13.3%increase in detection accuracy and a 23.8%increase in detection speed compared to the pre-improvement period.

关键词

飞鸟/小目标检测/上采样算子/NWD/注意力模块

Key words

flying bird/small target detection/upsampling operator/NWD/attention module

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出版年

2024
现代计算机
中大控股

现代计算机

影响因子:0.292
ISSN:1007-1423
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