基于卷积神经网络的无人机遥感影像水鸟自动识别  

Waterbirds Auto-Detection in Unmanned Aerial Vehicle Remote Sensing Image Base on Convolutional Neural Network

作　　者：林海 高大中 张童 崔国发[1] LIN Hai;GAO Da-Zhong;ZHANG Tong;CUI Guo-Fa(School of Ecology and Nature Conservation,Beijing Forestry University,Beijing 100083;East China Forest Inventory and Planning Institute,Hangzhou 310019,China)

机构地区：[1]北京林业大学生态与自然保护学院,北京100083 [2]国家林业和草原局华东调查规划院,杭州310019

出　　处：《动物学杂志》2024年第3期450-459,共10页Chinese Journal of Zoology

基　　金：国家自然科学基金项目(No.32171545)。

摘　　要：水鸟监测是了解水鸟种群和分布动态、开展水鸟和湿地保护的基础,但该活动耗时耗力。近年来,随着无人机遥感技术的发展,使用小型无人机获得高分辨率的水鸟遥感影像已经成为可能;与此同时,卷积神经网络提供了一种快速识别无人机遥感图像中的鸟类的方法。我们尝试结合两种技术,使用卷积神经网络Mask R-CNN与YOLOv3识别湖南西洞庭湖国家级自然保护区无人机遥感影像中的大型水鸟,取得了良好的效果:模型检测拍摄到的鸭属鸟类,包括绿翅鸭(Anas crecca)和罗纹鸭(A.falcata)的结果平均精度达到0.93,精度达到90.83%,召回率达到93%;检测小天鹅(Cygnus columbianus)的结果平均精度达到0.91,精度达到84.38%,召回率达到84.00%。结果表明,将无人机遥感技术与卷积神经网络结合,可以快速统计水鸟数量,在种群监测工作中具有应用潜力。[Objectives]Waterbird monitoring plays a crucial role in understanding population dynamics and guiding conservation efforts,but it has traditionally been a time-consuming process.In this study,our objective is to integrate unmanned aerial vehicle(UAV)remote sensing with convolutional neural networks(CNN)to achieve rapid and accurate estimation of waterbird populations.[Methods]We employed the DJI Mavic 2 Zoom UAV to capture high-resolution remote sensing images in the West Dongting Lake National Nature Reserve in Hunan.The UAV was flown at an altitude of 75 m,with its camera positioned in a vertically downward-facing orientation.We obtained images with a ground resolution of 1.2 cm/pixel,Table 1 displays the waterbirds captured in the images.We selected 503 images to construct a dataset,including two categories:Anas crecca/A.falcata and Cygnus columbianus,with 3778 and 395 samples respectively.The dataset has several training sets of different sizes(Table 2)and a validation set of 3032 samples.For each training set,we independently developed Mask R-CNN and YOLOv3 models,evaluating their performance using the validation set.Evaluation metrics include average precision,recall,precision,and F1-score.[Results]When identifying A.crecca/A.falcata,Mask R-CNN model achieved a recall rate of 93.00%and a precision of 90.83%(Table 4,Fig.4),while the YOLOv3 model achieved a recall rate of 93.00%and a precision of 88.79%(Table 5,Fig.5).After reaching 178 ind for A.crecca/A.falcata in the training set,further augmentation did not result in a significant improvement in the performance of both models.When identifying C.columbianus,the performance of both models improved with an increase in the size of the training set.The Mask R-CNN model achieved a recall rate of 84.00%and a precision of 84.38%(Table 6,Fig.6),while the YOLOv3 model achieved a recall rate of 90.00%and a precision of 81.69%(Table 7,Fig.7).The Mask R-CNN model detected images at a speed of approximately 12 images/s,while the YOLOv3 model detected images at a speed

关 键 词：水鸟 无人机 种群监测 卷积神经网络 

分 类 号：Q958[生物学—动物学]