A New Type-2 Fuzzy Algorithm for Unmanned Aerial Vehicle Image Segmentation

Zhong, Tingyu; Liu, Wenping; Luo, Youqing; Hung, Chih‐Cheng

doi:10.14257/ijmue.2017.12.5.07

IJMUE

2017

DOI: 10.14257/ijmue.2017.12.5.07

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A New Type-2 Fuzzy Algorithm for Unmanned Aerial Vehicle Image Segmentation

Tingyu Zhong¹,

Wenping Liu²,

Youqing Luo³

et al.

Abstract: This paper proposes a new type-2 fuzzy algorithm for the Unmanned Aerial Vehicle (UAV) image segmentation. In this algorithm, a logarithmic function is applied to transforming the membership of each pixel which is derived from the Fuzzy C-Means (FCM) algorithm; In other words, this logarithmic function defines a new membership function which can update the cluster centers effectively. The proposed method is called the Logarithmic Functional Fuzzy C-Means Algorithm (LF-FCM) used for the UAV image segmentation. … Show more

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2022

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A Detection Method for Individual Infected Pine Trees with Pine Wilt Disease Based on Deep Learning

Zhou

Liu

et al. 2022

Forests

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Pine wilt disease (PWD) can cause destructive death in many species of pine trees within a short period. The recognition of infected pine trees in unmanned aerial vehicle (UAV) forest images is a key technology for automatic monitoring and early warning of pests. This paper collected UAV visible and multispectral images of Korean pines (Pinus koraiensis) and Chinese pines (P. tabulaeformis) infected by PWD and divided the PWD infection into early, middle, and late stages. With the open-source annotation tool, LabelImg, we labeled the category of infected pine trees at each stage. After coordinate-correction preprocessing of the ground truth, the Korean pine and Chinese pine datasets were established. As a means of detecting infected pine trees of PWD and determining different infection stages, a multi-band image-fusion infected pine tree detector (MFTD) based on deep learning was proposed. Firstly, the Halfway Fusion mode was adopted to fuse the network based on four YOLOv5 variants. Simultaneously, the Backbone network was initially designed as a dual branching network that includes visible and multispectral subnets. Moreover, the features of visible and multispectral images were extracted. To fully utilize the features of visible and multispectral images, a multi-band feature fusion transformer (MFFT) with a multi-head attention mechanism and a feed-forward network was constructed to enhance the information correlation between visible and multispectral feature maps. Finally, following the MFFT module, the two feature maps were fused and input into Neck and Head to predict the categories and positions of infected pine trees. The best-performing MFTD model achieved the highest detection accuracy with mean average precision values (mAP@50) of 88.5% and 86.8% on Korean pine and Chinese pine datasets, respectively, which improved by 8.6% and 10.8% compared to the original YOLOv5 models trained only with visible images. In addition, the average precision values (AP@50) are 87.2%, 93.5%, and 84.8% for early, middle, and late stages on the KP dataset and 81.2%, 92.9%, and 86.2% on the CP dataset. Furthermore, the largest improvement is observed in the early stage with 14.3% and 11.6%, respectively. The results show that MFTD can accurately detect the infected pine trees, especially those at the early stage, and improve the early warning ability of PWD.

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A Detection Method for Individual Infected Pine Trees with Pine Wilt Disease Based on Deep Learning

Zhou

Liu

et al. 2022

Forests

View full text Add to dashboard Cite

show abstract

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A New Type-2 Fuzzy Algorithm for Unmanned Aerial Vehicle Image Segmentation

Cited by 1 publication

References 10 publications

A Detection Method for Individual Infected Pine Trees with Pine Wilt Disease Based on Deep Learning

A Detection Method for Individual Infected Pine Trees with Pine Wilt Disease Based on Deep Learning

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