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.
Conclusions: 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.
［摘要］ 目的 建立一种基于无人机影像深度学习算法的智能识别模型, 初步评价其用于血吸虫病家畜传染源耕牛远 程识别和监测管理的效果。方法 以环鄱阳湖地区有螺洲滩作为研究区域, 采用无人机航拍采集该区域影像数据集。对数据集进行增强处理, 并使用数据标注工具 VGG Image Annotator 标记样本数据库中的耕牛, 建立耕牛形态识别标签。基于 Mask R-卷积神经网络 (CNN) 深度学习算法建立智能识别模型用于识别耕牛分布, 采用准确率、精确率、召回率、F1 得分和平均精确率等指标对模型识别耕牛效果进行评价。结果 共获取200幅无人机航拍原始影像, 对影像数据增强 处理后获得410幅影像, 标记耕牛识别训练样本2 860个。构建的 Mask R-CNN 深度学习识别模型在迭代200轮后收敛, 模型准确率为88.01%、精确率为92.33%、召回率为94.06%、F1得分为93.19%、平均精确率为92.27%, 可有效检测和分割 耕牛形态特征。结论 基于无人机影像深度学习算法构建的Mask R-CNN模型识别耕牛准确性较高, 可用于血吸虫病家 畜传染源远程智能识别、监测和管理。.
Keywords: Cattle; Convolutional neural network; Deep learning; Image recognition; Schistosomiasis; Source of infection; Unmanned aerial vehicle.