期刊论文

Improved grain pest detection algorithm of YOLOv5

中文

武汉轻工大学学报

2095-7386

2023

42

03

34-40

10.3969/j.issn.2095-7386.2023.03.005

湖北省技术创新专项重大项目(2022BEC054)。

本校为第一机构

针对粮虫检测中因虫体尺寸小、身体颜色与环境接近、藏匿环境复杂而导致的误检和漏检等问题，提出一种基于深度学习技术的改进YOLOv5粮虫检测算法。该算法通过改进Mosaic模块和特征尺度模块，增强对小目标的感知能力；通过改进卷积模块，在不影响检测速度的同时，增加了检测精度。试验结果表明，相比原算法，改进算法的检测时间降低了4.3 ms,提升了3.2%的检测精度。改进算法解决了粮虫检测中存在的误检、漏检等问题，为进一步研究高效的粮虫检测方法提供了理论参考。

To address the problems of false detection and missed detection caused by the small size of grain pest,close body color and environment,and complex hiding environment in grain pest detection,an improved YOLOv5 grain pest detection algorithm based on deep learning techno...MORE To address the problems of false detection and missed detection caused by the small size of grain pest,close body color and environment,and complex hiding environment in grain pest detection,an improved YOLOv5 grain pest detection algorithm based on deep learning technology is proposed.This algorithm uses improved Mosaic...