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孙钰,周焱,袁明帅,刘文萍,骆有庆,宗世祥.基于深度学习的森林虫害无人机实时监测方法[J].农业工程学报,2018,34(21):74-81.DOI:10.11975/j.issn.1002-6819.2018.21.009
基于深度学习的森林虫害无人机实时监测方法
投稿时间:2018-06-13  修订日期:2018-09-10
中文关键词:  无人机  监测  虫害  目标检测  深度学习
基金项目:北京市科技计划“影响北京生态安全的重大钻蛀性害虫防控技术研究与示范”(Z171100001417005)
作者单位
孙钰 1. 北京林业大学信息学院北京 100083 
周焱 1. 北京林业大学信息学院北京 100083 
袁明帅 1. 北京林业大学信息学院北京 100083 
刘文萍 1. 北京林业大学信息学院北京 100083 
骆有庆 2. 北京林业大学林学院北京 100083 
宗世祥 2. 北京林业大学林学院北京 100083 
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全文下载次数: 89
中文摘要:无人机遥感是监测森林虫害的先进技术,但航片识别的实时性尚不能快速定位虫害爆发中心、追踪灾情发生发展。该文针对受红脂大小蠹危害的油松林,使用基于深度学习的目标检测技术,提出一种无人机实时监测方法。该方法训练精简的SSD300目标检测框架,无需校正拼接,直接识别无人机航片。改进的框架使用深度可分离卷积网络作为基础特征提取器,针对航片中目标尺寸删减预测模块,优化默认框的宽高比,降低模型的参数量和运算量,加快检测速度。试验选出的最优模型,测试平均查准率可达97.22%,在移动图形工作站图形处理器加速下,单张航片检测时间即可缩短至0.46 s。该方法简化了无人机航片的检测流程,可实现受害油松的实时检测和计数,提升森林虫害早期预警能力。
Sun Yu,Zhou Yan,Yuan Mingshuai,Liu Wenping,Luo Youqing,Zong Shixiang.UAV real-time monitoring for forest pest based on deep learning[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2018,34(21):74-81.DOI:10.11975/j.issn.1002-6819.2018.21.009
UAV real-time monitoring for forest pest based on deep learning
Author NameAffiliation
Sun Yu 1. School of Information, Beijing Forestry University, Beijing 100083, China
 
Zhou Yan 1. School of Information, Beijing Forestry University, Beijing 100083, China
 
Yuan Mingshuai 1. School of Information, Beijing Forestry University, Beijing 100083, China
 
Liu Wenping 1. School of Information, Beijing Forestry University, Beijing 100083, China
 
Luo Youqing 2. School of Forestry, Beijing Forestry University, Beijing 100083, China 
Zong Shixiang 2. School of Forestry, Beijing Forestry University, Beijing 100083, China 
Key words:unmanned aerial vehicle  monitoring  diseases  object detection  deep learning
Abstract: The unmanned aerial vehicle (UAV) remote sensing featured by low cost and flexibility offers a promising solution for pests monitoring by acquiring high resolution forest imagery. So the forest pest monitoring system based on UAV is essential to the early warning of red turpentine beetle (RTB) outbreaks. However, the UAV monitoring method based on image analysis technology suffers from inefficiency and depending on pre-processing, which prohibits the practical application of UAV remote sensing. Due to the long process flow, traditional methods can not locate the outbreak center and track the development of epidemic in time. The RTB is a major forestry invasive pest which damages the coniferous species of pine trees in northern China. This paper focuses on the detection of pines infected by RTBs. A real-time forest pest monitoring method based on deep learning is proposed for UAV forest imagery. The proposed method was consisted of three steps: 1) The UAV equipped with prime lens camera scans the infected forest and collects images at fixes points. 2) The Android client on UAV remote controller receives images and then requests the mobile graphics workstation for infected trees detection through TensorFlow Serving in real time. 3) The mobile graphics workstation runs a tailored SSD300 (single shot multibox detector) model with graphics processing unit (GPU) parallel acceleration to detect infected trees without orthorectification and image mosaic. Compared with Faster R-CNN and other two-stage object detection frameworks, SSD, as a lightweight object detection framework, shows the advantages of real-time and high accuracy. The original SSD300 object detection framework uses truncated VGG16 as basic feature extractor and the 6 layers (named P1-P6) prediction module to detect objects with different sizes. The proposed tailored SSD300 object detection framework includes two parts. First, a 13-layer depthwise separable convolution is used as basic feature extractor, which reduces several times computation overhead compared with the standard convolutions in VGG16. Second, most loss is derived from positive default boxes and these boxes mainly concentrated in P2 and P3 due to the constraints of crown size, UAV flying height and lens' focal length. Therefore, the tailored SSD300 retains only P2 and P3 as prediction module and the other prediction layers are deleted to further reduce computation overhead. Besides, aspect ratio of default boxes is set to {1, 2, 1/2}, since the aspect ratio of crown is approximate 1. The UAV imagery is collected on 6 experimental plots at 50-75 m height. The photos of No.2 experimental plot are considered as test set and the rest are train set. A total of 82 aerial photos are used in the experiment, including 70 photos in the train set and 12 photos in the test set. The AP and run time of five models are evaluated. The average precision (AP) of the tailored SSD300 model reaches up to 97.22%, which is lower than the AP of original SSD300. While the proposed model has only 18.8 MB parameters, reducing above 530 MB compared with the original model. And the run time is 0.46 s on a mobile workstation equipped with NVIDIA GTX 1050Ti GPU, while the original model needs 4.56 s. Experimental results demonstrate that the downsize of basic feature extractor and prediction module speed up detection with a little impact on AP. The maximum coverage of aerial photo captured at 75 m height is 38.18 m×50.95 m. When the UAV has a horizontal speed of 15 m/s, it takes 3.4 s to move to the next shooting point without overlap, longer than the detection time. Therefore, the proposed method can simplify the detection process of UAV monitoring and realizes the real-time detection of RTB damaged pines, which introduces a practical and applicable solution for early warning of RTB outbreaks.
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