期刊论文

Automatic recognition and classification of field insects based on lightweight deep learning model

中文

吉林大学学报(工学版)

1671-5497

2021

51

03

1131-1139

10.13229/j.cnki.jdxbgxb20200116

国家自然科学基金项目（31772157）； 中央引导地方科技发展专项资金项目（2019XF5015）； 湖南省教育厅科学研究项目（17C0757）；

本校为第一机构

由于田间昆虫环境的复杂性、昆虫类别间样本数量的不均衡性,现有田间昆虫自动识别和分类方法存在误识率高、效率低等缺点。本文基于轻量化深度学习模型提出了新的田间昆虫自动识别和分类算法。首先,对田间昆虫图像进行预处理,将其输入到轻量化算法中进行特征提取,通过多尺度特征融合输出不同尺寸的预测网络。然后,引入联合交并比进行田间昆虫自动识别和分类。最后,与其他算法进行了仿真对比实验,结果表明,本文算法的田间昆虫自动识别和分类正确率高、用时少、鲁棒性强,有效解决了昆虫堆积、背景干扰等问题,可实时、在线识别田间昆虫。

Due to the complexity of the insect environment in the field and the imbalance in the number of samples among insect categories,the existing automatic identification and classification methods for field insects have high misidentification rates and low efficiency. In this paper,a new field insect automatic identification and classification algorithm is developed based on a lightweight deep learning model. First, preprocessing applied to the picture,then those images were input to the lightweight algorithm for feature extraction,and multi-scale feature fusion were adopted to output prediction n...