Pine wilt disease spreading prevention system using semantic segmentation

Hwang, Chulhyun; Jeong, Jaean; Jung, Hoe-Kyung

doi:10.11591/ijece.v11i3.pp2666-2673

Cited by 3 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The encoder module allows us to extract features at an arbitrary resolution by applying atrous convolution [33]. DeepLabV3+ uses atrous separable convolution by combining atrous convolution with separable convolution in addition to the ResNet structure proposed in DeepLabV3 to solve the performance limitations of the existing fully convolutional network (FCN) [20]. In addition, low-level features were extracted using a deep convolutional neural network (DCNN) for the input image, and to obtain high-level features in the encoder, multi-scale targets were extracted after DCNN using atrous spatial pyramid pooling (ASPP).…”

Section: U-net Segnet Deeplabv3+ Algorithmsmentioning

confidence: 99%

“…The result showed approximately 57% accuracy for SegNet and 77% for YOLOv2. Hwang et al [20] evaluated the accuracy using semantic segmentation, an object detection model, to compensate for the weakness that the object recognition model is effective for object recognition but does not reflect the location, shape, and area of the object. The models used were SegNet, FCN, U-Net, and DeepLab3, and the average classification accuracy of each model was about 86.9%, with a maximum classification accuracy of about 90%.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Pine Wilt Disease Using Time Series UAV Imagery and Deep Learning Semantic Segmentation

Lee

Cho

Youm

et al. 2023

Forests

View full text Add to dashboard Cite

The purpose of this study was to enhance the detection accuracy for pine-wilt-diseased trees (PWDT) using time series UAV imagery (TSUI) and deep learning semantic segmentation (DLSS) techniques. The detailed methods to accomplish the research objectives were as follows. Considering the atypical and highly varied ecological characteristics of PWDT, DLSS algorithms of U-Net, SegNet, and DeepLab V3+ (ResNet18 and 50) were adopted. A total of 2350 PWDT were vectorized at 9 sites, and 795 images of 2000 damaged trees were used as training data and 200 images where 350 PWDT were found, were used as the test dataset. The felled trees were tracked and the pest-controlled trees were used as to ground truth the TSUI of at least 2 years to ensure the reliability of the constructed learning data. The results demonstrated that among the evaluated algorithms, DeepLab V3+ (ResNet50) achieved the best f1-score (0.742) and also provided the best recall (0.727). SegNet did not detect any shaded PWDT, but DeepLabV3+ (ResNet50) found most of the PWDT, especially those with atypical shapes near the felled trees. All algorithms except DeepLabV3+ (ResNet50) generated false positives for browned broadleaf trees. For the trees, all algorithms did not detect PWDT that had been dead for a long time and had lost most of their leaves or had turned gray. Most of the older PWDT have been logged, but for the few that remain, the relative lack of training data may be contributing to their poor detection. For land cover, the false positives occurred mainly in bare ground, shaded areas, roads, and rooftops. This study thus verified the potential use of semantic segmentation in the detection of forest diseases such as PWD, while the detection accuracy is anticipated to increase with the acquisition of adequate quantities of learning data in future.

show abstract

Section: U-net Segnet Deeplabv3+ Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Pine Wilt Disease Using Time Series UAV Imagery and Deep Learning Semantic Segmentation

Lee

Cho

Youm

et al. 2023

Forests

View full text Add to dashboard Cite

show abstract

“…To address the limitations of object recognition models in detecting PWD, Hwang et al [17] investigated the accuracy of semantic segmentation, which excels at recognizing objects but typically lacks the ability to precisely capture their location, shape, or area. They evaluated various semantic segmentation models such as SegNet, FCN, U-Net, and DeepLabv3, achieving a maximum accuracy of up to 90%.…”

Section: Introductionmentioning

confidence: 99%

Pine Wilt Disease Segmentation with Deep Metric Learning Species Classification for Early-Stage Disease and Potential False Positive Identification

Thapa,

Khanal,

Bhattarai

et al. 2024

Electronics

View full text Add to dashboard Cite

Pine Wilt Disease poses a significant global threat to forests, necessitating swift detection methods. Conventional approaches are resource-intensive but utilizing deep learning on ortho-mapped images obtained from Unmanned Aerial Vehicles offers cost-effective and scalable solutions. This study presents a novel method for Pine Wilt Disease detection and classification using YOLOv8 for segmenting diseased areas, followed by cropping the diseased regions from the original image and applying Deep Metric Learning for classification. We trained a ResNet50 model using semi-hard triplet loss to obtain embeddings, and subsequently trained a Random Forest classifier tasked with identifying tree species and distinguishing false positives. Segmentation was favored over object detection due to its ability to provide pixel-level information, enabling the flexible extension of subsequent bounding boxes. Deep Metric Learning-based classification after segmentation was chosen for its effectiveness in handling visually similar images. The results indicate a mean Intersection over Union of 83.12% for segmentation, with classification accuracies of 98.7% and 90.7% on the validation and test sets, respectively.

show abstract

“…This paper proposes semantic segmentation to extract road and classify lane marks for LDW. The use of semantic segmentation is very broad, such as in agriculture [7], biomedical [8], [9], and smart city [10]. Babaali's research use semantic segmentation to extract road from remote sensing image.…”

Section: Introductionmentioning

confidence: 99%

Real-time lane departure warning with cascade lane segmentation

Indrabayu,

Prayogi,

Areni

et al. 2023

IJEECS

View full text Add to dashboard Cite

<span>Lane departure warning (LDW) is one of the safety innovations in autonomous cars that provides vehicle position monitoring. This technology will alarm if the vehicle moves out of the lane. Lane detection and lane measurement is the main part of LDW. The novelty of this research is the method can measure different lane marks. Very important to know the lane mark that can and cannot be passed. We use semantic segmentation to segment lane mark solid, lane mark dashed, and road. After extracting road and lane marks, we use inverse perspective mapping (IPM) to help calculate the measurement between the car and lane mark. The data is that 374 images were collected from several roads in Makassar City. The model was evaluated using intersection over union (IoU), reaching 79.8% accuracy. The developed system also estimates the measure between the vehicle and lane marks. <br /> The lane measurement estimation system’s test results were evaluated using the root mean square error (RMSE) method to reach between 0.025254 and 0.134345.</span>

show abstract

Pine wilt disease spreading prevention system using semantic segmentation

Cited by 3 publications

References 10 publications

Detection of Pine Wilt Disease Using Time Series UAV Imagery and Deep Learning Semantic Segmentation

Detection of Pine Wilt Disease Using Time Series UAV Imagery and Deep Learning Semantic Segmentation

Pine Wilt Disease Segmentation with Deep Metric Learning Species Classification for Early-Stage Disease and Potential False Positive Identification

Real-time lane departure warning with cascade lane segmentation

Contact Info

Product

Resources

About