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基于Faster R-CNN的松材線蟲(chóng)病受害木識(shí)別與定位
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國(guó)家自然科學(xué)基金項(xiàng)目(41571423,、41764002)


Detection and Location of Pine Wilt Disease Induced Dead Pine Trees Based on Faster R-CNN
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    摘要:

    松材線蟲(chóng)病是一種毀滅性松樹(shù)傳染病,,其傳播速度快、發(fā)病時(shí)間短,、致病力強(qiáng),,及時(shí)發(fā)現(xiàn)、確定受害木的位置,,并采取安全處理措施是目前控制松材線蟲(chóng)病蔓延的有效手段,。本文通過(guò)小型無(wú)人機(jī)搭載可見(jiàn)光RGB數(shù)碼相機(jī)獲取超高空間分辨率影像,采用Faster R-CNN目標(biāo)檢測(cè)算法實(shí)現(xiàn)對(duì)染病變色松樹(shù)的自動(dòng)識(shí)別,,與傳統(tǒng)受害木識(shí)別方法不同,,本文考慮了其他枯死樹(shù)和紅色闊葉樹(shù)對(duì)受害木識(shí)別的影響。實(shí)驗(yàn)結(jié)果表明,,根據(jù)受害木的冠幅大小修改區(qū)域生成網(wǎng)絡(luò)中的錨框(anchor)尺寸,,并考慮其他枯死樹(shù)和紅色闊葉樹(shù)的影響,有利于提高受害木識(shí)別效果和檢測(cè)精度,。改進(jìn)后受害木識(shí)別總體精度從75.64%提高到82.42%,,提高了6.78個(gè)百分點(diǎn),能夠滿足森林防護(hù)人員對(duì)受害木定位處理的需求,。通過(guò)坐標(biāo)轉(zhuǎn)換的方式得到受害木的精確位置信息與空間分布情況,,結(jié)合點(diǎn)位合并過(guò)程,最終正確定位出494棵受害木,。本文通過(guò)無(wú)人機(jī)遙感結(jié)合目標(biāo)檢測(cè)算法能監(jiān)測(cè)松材線蟲(chóng)病的發(fā)生和獲取受害木的分布情況,,可為松材線蟲(chóng)病的防控提供技術(shù)支持。

    Abstract:

    Pine wilt disease (PWD) is a devastating infectious disease for the rapid spread, short disease period, and strong pathogenic ability. At present, detecting the PWD induced dead pine trees (DPT) timely and then taking corresponding measures are vital to control the spread of PWD. An unmanned aerial vehicle (UAV) platform equipped with the Vis-RGB digital camera was used to obtain the ultra-high spatial resolution images. Deep learning object detection of Faster R-CNN was adopted to detect the DPT automatically. Different from the previous research on the DPT identification, the influences of other dead trees and red broadleaved trees on DPT identification were considered. The results showed that Faster R-CNN can effectively identify the DPT. The 6.78 percentage points detection accuracy of the DPT would be improved when taking the anchor size, other dead trees and red broad-leaved trees into consideration. The overall accuracy of DPT detection can reach 82.42%, which can meet the protector for felling of the DPT. Finally, the position of predicted DPT was calculated accurately using coordinate transformation. Combined with the point combination process, 494 DPT were correctly located. This research had the advantages of low cost, high efficiency and automatic identification, and can provide technical support for the prevention and control of PWD. The combination of UAV remote sensing and object detection algorithms was a promising method to monitor the occurrence of PWD and the distribution of the DPT, which provided important basis for the consequence harmless treatment of PWD induced DPT.

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徐信羅,陶歡,李存軍,程成,郭杭,周靜平.基于Faster R-CNN的松材線蟲(chóng)病受害木識(shí)別與定位[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2020,51(7):228-236. XU Xinluo, TAO Huan, LI Cunjun, CHENG Cheng, GUO Hang, ZHOU Jingping. Detection and Location of Pine Wilt Disease Induced Dead Pine Trees Based on Faster R-CNN[J]. Transactions of the Chinese Society for Agricultural Machinery,2020,51(7):228-236.

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  • 收稿日期:2019-11-06
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  • 在線發(fā)布日期: 2020-07-10
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