Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

Large-scale mapping of date palm trees is vital for their consistent monitoring and sustainable management, considering their substantial commercial, environmental, and cultural value. This study presents an automatic approach for the large-scale mapping of date palm trees from very-high-spatial-resolution (VHSR) unmanned aerial vehicle (UAV) datasets, based on a deep learning approach. A U-Shape convolutional neural network (U-Net), based on a deep residual learning framework, was developed for the semantic segmentation of date palm trees. A comprehensive set of labeled data was established to enable the training and evaluation of the proposed segmentation model and increase its generalization capability. The performance of the proposed approach was compared with those of various state-of-the-art fully convolutional networks (FCNs) with different encoder architectures, including U-Net (based on VGG-16 backbone), pyramid scene parsing network, and two variants of DeepLab V3+. Experimental results showed that the proposed model outperformed other FCNs in the validation and testing datasets. The generalizability evaluation of the proposed approach on a comprehensive and complex testing dataset exhibited higher classification accuracy and showed that date palm trees could be automatically mapped from VHSR UAV images with an F-score, mean intersection over union, precision, and recall of 91%, 85%, 0.91, and 0.92, respectively. The proposed approach provides an efficient deep learning architecture for the automatic mapping of date palm trees from VHSR UAV-based images.

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Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽

10.3390/s21082852 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2852

Author(s):

Parvathaneni Naga Srinivasu ◽

Jalluri Gnana SivaSai ◽

Muhammad Fazal Ijaz ◽

Akash Kumar Bhoi ◽

Wonjoon Kim ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Skin Disease ◽

Network Architecture ◽

Large Scale ◽

Short Term Memory ◽

Convolutional Networks ◽

Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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Mapping Single Palm-Trees Species in Forest Environments with a Deep Convolutional Neural Network

10.20944/preprints202103.0220.v1 ◽

2021 ◽

Author(s):

Luciene Sales Dagher Arce ◽

Mauro dos Santos de Arruda ◽

Danielle Elis Garcia Furuya ◽

Lucas Prado Osco ◽

Ana Paula Marques Ramos ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Tree Species ◽

State Of The Art ◽

Palm Tree ◽

Forest Environment ◽

Individual Tree ◽

Palm Trees ◽

Rgb Images

Accurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB scenes, which can be a hindrance for most automatic methods. State-of-the-art deep learning methods could be capable of identifying tree species with an attractive cost, accuracy, and computational load in RGB images. This paper presents a deep learning-based approach to detect an important multi-use species of palm trees (Mauritia flexuosa; i.e., Buriti) on aerial RGB imagery. In South-America, this palm tree is essential for many indigenous and local communities because of its characteristics. The species is also a valuable indicator of water resources, which comes as a benefit for mapping its location. The method is based on a Convolutional Neural Network (CNN) to identify and geolocate singular tree species in a high-complexity forest environment, and considers the likelihood of every pixel in the image to be recognized as a possible tree by implementing a confidence map feature extraction. This study compares the performance of the proposed method against state-of-the-art object detection networks. For this, images from a dataset composed of 1,394 airborne scenes, where 5,334 palm-trees were manually labeled, were used. The results returned a mean absolute error (MAE) of 0.75 trees and an F1-measure of 86.9%. These results are better than both Faster R-CNN and RetinaNet considering equal experiment conditions. The proposed network provided fast solutions to detect the palm trees, with a delivered image detection of 0.073 seconds and a standard deviation of 0.002 using the GPU. In conclusion, the method presented is efficient to deal with a high-density forest scenario and can accurately map the location of single species like the M flexuosa palm tree and may be useful for future frameworks.

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Automatic Deep Learning Semantic Segmentation of Ultrasound Thyroid Cineclips using Recurrent Fully Convolutional Networks

IEEE Access ◽

10.1109/access.2020.3045906 ◽

2020 ◽

pp. 1-1

Author(s):

Jeremy M. Webb ◽

Duane D. Meixner ◽

Shaheeda A. Adusei ◽

Eric C. Polley ◽

Mostafa Fatemi ◽

...

Keyword(s):

Deep Learning ◽

Semantic Segmentation ◽

Convolutional Networks ◽

Fully Convolutional Networks

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Automatic Tunnel Crack Detection Based on U-Net and a Convolutional Neural Network with Alternately Updated Clique

Sensors ◽

10.3390/s20030717 ◽

2020 ◽

Vol 20 (3) ◽

pp. 717 ◽

Cited By ~ 1

Author(s):

Gang Li ◽

Biao Ma ◽

Shuanhai He ◽

Xueli Ren ◽

Qiangwei Liu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Crack Width ◽

Crack Detection ◽

Detection Method ◽

Learning Algorithm ◽

Semantic Segmentation ◽

Fully Convolutional Networks ◽

Deep Learning Algorithm ◽

Mean Width

Regular crack inspection of tunnels is essential to guarantee their safe operation. At present, the manual detection method is time-consuming, subjective and even dangerous, while the automatic detection method is relatively inaccurate. Detecting tunnel cracks is a challenging task since cracks are tiny, and there are many noise patterns in the tunnel images. This study proposes a deep learning algorithm based on U-Net and a convolutional neural network with alternately updated clique (CliqueNet), called U-CliqueNet, to separate cracks from background in the tunnel images. A consumer-grade DSC-WX700 camera (SONY, Wuxi, China) was used to collect 200 original images, then cracks are manually marked and divided into sub-images with a resolution of 496 × 496 pixels. A total of 60,000 sub-images were obtained in the dataset of tunnel cracks, among which 50,000 were used for training and 10,000 were used for testing. The proposed framework conducted training and testing on this dataset, the mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and F1-score are 92.25%, 86.96%, 86.32% and 83.40%, respectively. We compared the U-CliqueNet with fully convolutional networks (FCN), U-net, Encoder–decoder network (SegNet) and the multi-scale fusion crack detection (MFCD) algorithm using hypothesis testing, and it’s proved that the MIoU predicted by U-CliqueNet was significantly higher than that of the other four algorithms. The area, length and mean width of cracks can be calculated, and the relative error between the detected mean crack width and the actual mean crack width ranges from −11.20% to 18.57%. The results show that this framework can be used for fast and accurate crack semantic segmentation of tunnel images.

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A Novel Object-Based Deep Learning Framework for Semantic Segmentation of Very High-Resolution Remote Sensing Data: Comparison with Convolutional and Fully Convolutional Networks

Remote Sensing ◽

10.3390/rs11060684 ◽

2019 ◽

Vol 11 (6) ◽

pp. 684 ◽

Cited By ~ 17

Author(s):

Maria Papadomanolaki ◽

Maria Vakalopoulou ◽

Konstantinos Karantzalos

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Semantic Segmentation ◽

Novel Object ◽

Convolutional Networks ◽

Learning Framework ◽

Fully Convolutional Networks ◽

Object Based ◽

Deep Networks ◽

Very High

Deep learning architectures have received much attention in recent years demonstrating state-of-the-art performance in several segmentation, classification and other computer vision tasks. Most of these deep networks are based on either convolutional or fully convolutional architectures. In this paper, we propose a novel object-based deep-learning framework for semantic segmentation in very high-resolution satellite data. In particular, we exploit object-based priors integrated into a fully convolutional neural network by incorporating an anisotropic diffusion data preprocessing step and an additional loss term during the training process. Under this constrained framework, the goal is to enforce pixels that belong to the same object to be classified at the same semantic category. We compared thoroughly the novel object-based framework with the currently dominating convolutional and fully convolutional deep networks. In particular, numerous experiments were conducted on the publicly available ISPRS WGII/4 benchmark datasets, namely Vaihingen and Potsdam, for validation and inter-comparison based on a variety of metrics. Quantitatively, experimental results indicate that, overall, the proposed object-based framework slightly outperformed the current state-of-the-art fully convolutional networks by more than 1% in terms of overall accuracy, while intersection over union results are improved for all semantic categories. Qualitatively, man-made classes with more strict geometry such as buildings were the ones that benefit most from our method, especially along object boundaries, highlighting the great potential of the developed approach.

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Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning

Scientific Reports ◽

10.1038/s41598-021-04354-w ◽

2022 ◽

Vol 12 (1) ◽

Author(s):

Shota Ito ◽

Yuichi Mine ◽

Yuki Yoshimi ◽

Saori Takeda ◽

Akari Tanaka ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Magnetic Resonance ◽

Convolutional Neural Network ◽

Temporomandibular Joint ◽

Temporomandibular Disorders ◽

Temporomandibular Disorder ◽

Semantic Segmentation ◽

Magnetic Resonance Images ◽

Articular Disc

AbstractTemporomandibular disorders are typically accompanied by a number of clinical manifestations that involve pain and dysfunction of the masticatory muscles and temporomandibular joint. The most important subgroup of articular abnormalities in patients with temporomandibular disorders includes patients with different forms of articular disc displacement and deformation. Here, we propose a fully automated articular disc detection and segmentation system to support the diagnosis of temporomandibular disorder on magnetic resonance imaging. This system uses deep learning-based semantic segmentation approaches. The study included a total of 217 magnetic resonance images from 10 patients with anterior displacement of the articular disc and 10 healthy control subjects with normal articular discs. These images were used to evaluate three deep learning-based semantic segmentation approaches: our proposed convolutional neural network encoder-decoder named 3DiscNet (Detection for Displaced articular DISC using convolutional neural NETwork), U-Net, and SegNet-Basic. Of the three algorithms, 3DiscNet and SegNet-Basic showed comparably good metrics (Dice coefficient, sensitivity, and positive predictive value). This study provides a proof-of-concept for a fully automated deep learning-based segmentation methodology for articular discs on magnetic resonance images, and obtained promising initial results, indicating that the method could potentially be used in clinical practice for the assessment of temporomandibular disorders.

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Large-Scale Text Classification Using Scope-Based Convolutional Neural Network: A Deep Learning Approach

IEEE Access ◽

10.1109/access.2019.2955924 ◽

2019 ◽

Vol 7 ◽

pp. 171548-171558 ◽

Cited By ~ 2

Author(s):

Jiaying Wang ◽

Yaxin Li ◽

Jing Shan ◽

Jinling Bao ◽

Chuanyu Zong ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Text Classification ◽

Large Scale ◽

Learning Approach

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The Application of Deep Learning and Image Processing Technology in Laser Positioning

Applied Sciences ◽

10.3390/app8091542 ◽

2018 ◽

Vol 8 (9) ◽

pp. 1542 ◽

Cited By ~ 7

Author(s):

Chern-Sheng Lin ◽

Yu-Chia Huang ◽

Shih-Hua Chen ◽

Yu-Liang Hsu ◽

Yu-Chen Lin

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Template Matching ◽

Large Scale ◽

Laser Spot ◽

Precise Position ◽

Spot Area ◽

Target Spot ◽

Spot Image

In this study, machine vision technology was used to precisely position the highest energy of the laser spot to facilitate the subsequent joining of product workpieces in a laser welding machine. The displacement stage could place workpieces into the superposition area and allow the parts to be joined. With deep learning and a convolutional neural network training program, the system could enhance the accuracy of the positioning and enhance the efficiency of the machine work. A bi-analytic deep learning localization method was proposed in this study. A camera was used for real-time monitoring. The first step was to use a convolutional neural network to perform a large-scale preliminary search and locate the laser light spot region. The second step was to increase the optical magnification of the camera, re-image the spot area, and then use template matching to perform high-precision repositioning. According to the aspect ratio of the search result area, the integrity parameters of the target spot were determined. The centroid calculation was performed in the complete laser spot. If the target was an incomplete laser spot, the operation of invariant moments would be performed. Based on the result, the precise position of the highest energy of the laser spot could be obtained from the incomplete laser spot image. The amount of displacement could be calculated by overlapping the highest energy of the laser spot and the center of the image.

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An ECG Signal Classification Method Based on Dilated Causal Convolution

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/6627939 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Hao Ma ◽

Chao Chen ◽

Qing Zhu ◽

Haitao Yuan ◽

Liming Chen ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Recurrent Neural Network ◽

Signal Classification ◽

Accuracy Rate ◽

Ecg Signals ◽

Convolutional Networks ◽

Fully Convolutional Networks ◽

Output Time

The incidence of cardiovascular disease is increasing year by year and is showing a younger trend. At the same time, existing medical resources are tight. The automatic detection of ECG signals becomes increasingly necessary. This paper proposes an automatic classification of ECG signals based on a dilated causal convolutional neural network. To solve the problem that the recurrent neural network framework network cannot be accelerated by hardware equipment, the dilated causal convolutional neural network is adopted. Given the features of the same input and output time steps of the recurrent neural network and the nondisclosure of future information, the network is constructed with fully convolutional networks and causal convolution. To reduce the network depth and prevent gradient explosion or gradient disappearance, the dilated factor is introduced into the model, and the residual blocks are introduced into the model according to the shortcut connection idea. The effectiveness of the algorithm is verified in the MIT-BIH Atrial Fibrillation Database (MIT-BIH AFDB). In the experiment of the MIT-BIH AFDB database, the classification accuracy rate is 98.65%.

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Deep Learning Image Feature Recognition Algorithm for Judgment on the Rationality of Landscape Planning and Design

Complexity ◽

10.1155/2021/9921095 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Bin Hu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Feature Recognition ◽

Semantic Segmentation ◽

Landscape Patterns ◽

Image Feature ◽

Accuracy Rate ◽

Landscape Elements ◽

Landscape Element

This paper uses an improved deep learning algorithm to judge the rationality of the design of landscape image feature recognition. The preprocessing of the image is proposed to enhance the data. The deficiencies in landscape feature extraction are further addressed based on the new model. Then, the two-stage training method of the model is used to solve the problems of long training time and convergence difficulties in deep learning. Innovative methods for zoning and segmentation training of landscape pattern features are proposed, which makes model training faster and generates more creative landscape patterns. Because of the impact of too many types of landscape elements in landscape images, traditional convolutional neural networks can no longer effectively solve this problem. On this basis, a fully convolutional neural network model is designed to perform semantic segmentation of landscape elements in landscape images. Through the method of deconvolution, the pixel-level semantic segmentation is realized. Compared with the 65% accuracy rate of the convolutional neural network, the fully convolutional neural network has an accuracy rate of 90.3% for the recognition of landscape elements. The method is effective, accurate, and intelligent for the classification of landscape element design, which better improves the accuracy of classification, greatly reduces the cost of landscape element design classification, and ensures that the technical method is feasible. This paper classifies landscape behavior based on this model for full convolutional neural network landscape images and demonstrates the effectiveness of using the model. In terms of landscape image processing, the image evaluation provides a certain basis.

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