计算机应用与软件
計算機應用與軟件
계산궤응용여연건
COMPUTER APPLICATIONS AND SOFTWARE
2015年
4期
145-148
,共4页
图像分割%轮廓矩%灰度图像%神经网络%果蝇求偶
圖像分割%輪廓矩%灰度圖像%神經網絡%果蠅求偶
도상분할%륜곽구%회도도상%신경망락%과승구우
Image segmentation%Contour moment%Grayscale image%Neural network%Drosophila courtship
监控害虫求偶行为是农业病虫害防治的重要手段，传统的人工识别工作量大、效率低下，为实现自动检测与识别果蝇求偶行为，设计了基于图像分割和轮廓矩的识别方法。该方法首先采用基于灰度图像的背景生成、更新和阈值分割的方法自适应地获取果蝇的轮廓。然后根据轮廓矩理论，提取各果蝇轮廓的矩不变量并输入BP神经网络判断是否发生单侧振翅行为。最后根据果蝇间的位置关系，判断振翅果蝇是否在尾随其他果蝇，从而确定其是否发生了求偶行为。采用人工检测的方法进行对比验证，结果表明该方法能够有效地识别果蝇的求偶行为，准确识别率达89．6％。
鑑控害蟲求偶行為是農業病蟲害防治的重要手段，傳統的人工識彆工作量大、效率低下，為實現自動檢測與識彆果蠅求偶行為，設計瞭基于圖像分割和輪廓矩的識彆方法。該方法首先採用基于灰度圖像的揹景生成、更新和閾值分割的方法自適應地穫取果蠅的輪廓。然後根據輪廓矩理論，提取各果蠅輪廓的矩不變量併輸入BP神經網絡判斷是否髮生單側振翅行為。最後根據果蠅間的位置關繫，判斷振翅果蠅是否在尾隨其他果蠅，從而確定其是否髮生瞭求偶行為。採用人工檢測的方法進行對比驗證，結果錶明該方法能夠有效地識彆果蠅的求偶行為，準確識彆率達89．6％。
감공해충구우행위시농업병충해방치적중요수단，전통적인공식별공작량대、효솔저하，위실현자동검측여식별과승구우행위，설계료기우도상분할화륜곽구적식별방법。해방법수선채용기우회도도상적배경생성、경신화역치분할적방법자괄응지획취과승적륜곽。연후근거륜곽구이론，제취각과승륜곽적구불변량병수입BP신경망락판단시부발생단측진시행위。최후근거과승간적위치관계，판단진시과승시부재미수기타과승，종이학정기시부발생료구우행위。채용인공검측적방법진행대비험증，결과표명해방법능구유효지식별과승적구우행위，준학식별솔체89．6％。
Monitoring pests’courtship behaviours is a major means in agricultural pest and diseases management.Traditional manual identification has heavy workload and low efficiency.In order to detect and identify the courtship behaviours of drosophilae automatically,we design an identification method which is based on image segmentation and contour moment.First,the method employs the grayscale image-based methods of background generation,update and threshold segmentation to adaptively obtain the contours of drosophila.Then according to the theory of contour moments,it extracts the contour moment invariants of every drosophila contour and inputs them into BP neural network to estimate whether the drosophila vibrates its unilateral wing.In third step,it judges whether or not the wing-vibrating drosophila is trailing other drosophilae according to the position relationship between them so as to determine if drosophila has the courtship behaviour happened. Contrast verification is carried out in manual detection way,results show that the new method can effectively identify the courtship behaviour of drosophila and the accuracy of recognition reaches 89.6%.
