基于改进Faster R-CNN的肺结核病原体检测

Tuberculosis Pathogen Detection Based on Improved Faster R-CNN

本文提出一种基于Faster R-CNN的肺结核病原体检测方法,以更高的准确率和更低的漏检率检测肺结核.首先,通过Mosaic数据增强方法对数据集进行扩充,提高模型泛化能力,同时引入K-means聚类算法,对所用数据集重新聚类来生成对的锚点初始候选框大小.其次,将Faster R-CNN中的原始特征提取网络替换为Res2Net,并将其卷积核全部替换为空洞卷积,在与原卷积相比参数量不变的情况下,增大了感受野.再者,引入改进后的GC-FPN模块,使模型在轻量化的同时更好的关注小目标信息.最后,引入ROI Align,解决候选框和初始回归位置存在偏差的问题.实验结果表明,在公开数据集上,改进的Faster R-CNN模型与原本的Faster R-CNN算法相比,准确率提高了2.7%,召回率提升了1.4%,该算法不仅在结核图像数据集上得到了验证,而且具有较高的准确率.

In this study,a detection method for tuberculosis pathogens based on Faster R-CNN is proposed to detect tuberculosis with higher accuracy and lower missed detection rate.First,the Mosaic data enhancement method is used to expand the dataset to improve the generalization ability of the model.At the same time,the K-means clustering algorithm is introduced to re-cluster the used dataset to generate the initial candidate box size of the paired anchor points.Secondly,the original feature extraction network in Faster R-CNN is replaced with Res2Net,and all its convolution kernels are replaced with empty convolution.This can bring a larger receptive field compared with the original convolution when the number of parameters remains unchanged.Furthermore,the improved GC-FPN module is introduced to make the model pay more attention to small target information while being lightweight.Finally,ROI Align is introduced to solve the problem of deviation between the candidate box and the initial regression position.The experimental results show that,compared with the original Faster R-CNN algorithm,the improved Faster R-CNN model has a 2.7%higher accuracy and an 1.4%higher recall rate on the open data set.This algorithm has been verified on the dataset of tuberculosis images and possesses high accuracy.