Multichannel Object Detection for Detecting Suspected Trees With Pine Wilt Disease Using Multispectral Drone Imagery

Abstract

In this article, a multichannel convolutional neural network (CNN) based object detection was used to detect suspected trees of pine wilt disease after acquiring aerial photographs through a rotorcraft drone equipped with a multispectral camera. The acquired multispectral aerial photographs consist of RGB, green, red, NIR, and red edge spectral bands per shooting point. The aerial photographs for each band performed image calibration to correct radiation distortion, image alignment to correct the distance error of the lenses of a multispectral camera, and image enhancement to edge enhancement to highlight the features of objects in the image. After that, a large amount of data obtained through data augmentation were put into multichannel CNN-based object detection for training and test. As a result of verifying the detection performance of the trained model, excellent detection results were obtained with mAP 86.63% and average intersection over union 71.47%.

Keywords

• Convolutional neural network (CNN)
• deep learning
• drone
• multispectral
• pine wilt disease (PWD)
• remote sensing