Study on Thinking the Negative and Positive Samples Balance for the Pine Wild Disease Tree Detect Use Deep Learning
LU Tao
Abstract：Pine wilt disease(PWD)is a rapidly spreading and devastating forest disease that its control lies in timely detection of infected wood.To address the problem of generating candidate boxes with small target scale and multiple interference factors in karst mountain forests,which leads to difficulty in candidate box generation and imbalanced samples,a disease detection algorithm SB-HEM(sampling-balance-based hard example mining,SB-HEM)based on IoU sampling balance is proposed.The detection performance of each part of SB-HEM is evaluated from the perspectives of detection accuracy and detection speed.The experimental results show that the F1 value and mAP of SB-HEM infected wood detection are 78.96％,81.66％,respectively.SB-HEM effectively improves the network model's ability to identify negative samples,thereby achieving precise identification of infected wood for PWD,providing technical reference for PWD control.
Keywords：pine wild diseaseFaster R-CNNthe negative/positive samplessampling balance
Publication Date：2024-12-28
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：6( 82-87 )