Intelligent identification of livestock, a source of Schistosoma japonicum infection, based on deep learning of unmanned aerial vehicle images
10.16250/j.32.1374.2022273
- VernacularTitle:基于无人机影像深度学习算法的血吸虫病 家畜传染源智能识别研究
- Author:
Jingbo XUE
1
,
2
;
Shang XIA
1
,
2
;
Zhaojun LI
3
;
Xinyi WANG
1
;
Liangyu HUANG
1
;
Runchao HE
1
;
Shizhu LI
1
,
2
Author Information
1. National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai 200025, China
2. School of Global Health, Shanghai Jiao Tong University School of Medicine and Chinese Center for Tropical Diseases Research, Shanghai 200025, China
3. Jiangxi Provincial Institute of Parasitic Diseases Control, Jiangxi Provincial Key Laboratory of Schistosomiasis Prevention and Control, China
- Publication Type:Journal Article
- Keywords:
Schistosomiasis;
Source of infection;
Deep learning;
Unmanned aerial vehicle;
Image recognition;
Convolutional neural network;
Cattle
- From:
Chinese Journal of Schistosomiasis Control
2023;35(2):121-127
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop an intelligent recognition model based on deep learning algorithms of unmanned aerial vehicle (UAV) images, and to preliminarily explore the value of this model for remote identification, monitoring and management of cattle, a source of Schistosoma japonicum infection. Methods Oncomelania hupensis snail-infested marshlands around the Poyang Lake area were selected as the study area. Image datasets of the study area were captured by aerial photography with UAV and subjected to augmentation. Cattle in the sample database were annotated with the annotation software VGG Image Annotator to create the morphological recognition labels for cattle. A model was created for intelligent recognition of livestock based on deep learning-based Mask R-convolutional neural network (CNN) algorithms. The performance of the model for cattle recognition was evaluated with accuracy, precision, recall, F1 score and mean precision. Results A total of 200 original UAV images were obtained, and 410 images were yielded following data augmentation. A total of 2 860 training samples of cattle recognition were labeled. The created deep learning-based Mask R-CNN model converged following 200 iterations, with an accuracy of 88.01%, precision of 92.33%, recall of 94.06%, F1 score of 93.19%, and mean precision of 92.27%, and the model was effective to detect and segment the morphological features of cattle. Conclusion The deep learning-based Mask R-CNN model is highly accurate for recognition of cattle based on UAV images, which is feasible for remote intelligent recognition, monitoring, and management of the source of S. japonicum infection.
