scholarly journals Deep Learning for Detection of Visible Land Boundaries from UAV Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2077
Author(s):  
Bujar Fetai ◽  
Matej Račič ◽  
Anka Lisec
Keyword(s):  
Deep Learning ◽  
High Accuracy ◽  
Commercial Software ◽  
Advantages And Disadvantages ◽  
Cadastral Maps ◽  
First Case ◽  
Complex Approach ◽  
Aerial Vehicle ◽  
Speed Up ◽  
Boundary Mapping

Current efforts aim to accelerate cadastral mapping through innovative and automated approaches and can be used to both create and update cadastral maps. This research aims to automate the detection of visible land boundaries from unmanned aerial vehicle (UAV) imagery using deep learning. In addition, we wanted to evaluate the advantages and disadvantages of programming-based deep learning compared to commercial software-based deep learning. For the first case, we used the convolutional neural network U-Net, implemented in Keras, written in Python using the TensorFlow library. For commercial software-based deep learning, we used ENVINet5. UAV imageries from different areas were used to train the U-Net model, which was performed in Google Collaboratory and tested in the study area in Odranci, Slovenia. The results were compared with the results of ENVINet5 using the same datasets. The results showed that both models achieved an overall accuracy of over 95 %. The high accuracy is due to the problem of unbalanced classes, which is usually present in boundary detection tasks. U-Net provided a recall of 0.35 and a precision of 0.68 when the threshold was set to 0.5. A threshold can be viewed as a tool for filtering predicted boundary maps and balancing recall and precision. For equitable comparison with ENVINet5, the threshold was increased. U-Net provided more balanced results, a recall of 0.65 and a precision of 0.41, compared to ENVINet5 recall of 0.84 and a precision of 0.35. Programming-based deep learning provides a more flexible yet complex approach to boundary mapping than software-based, which is rigid and does not require programming. The predicted visible land boundaries can be used both to speed up the creation of cadastral maps and to automate the revision of existing cadastral maps and define areas where updates are needed. The predicted boundaries cannot be considered final at this stage but can be used as preliminary cadastral boundaries.

scholarly journals A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6540
Author(s):  
Qian Pan ◽  
Maofang Gao ◽  
Pingbo Wu ◽  
Jingwen Yan ◽  
Shilei Li
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Recognition Accuracy ◽  
Yellow Rust ◽  
Semantic Segmentation ◽  
High Accuracy ◽  
Economic Losses ◽  
Support Vector ◽  
Aerial Vehicle ◽  
Uav Images

Yellow rust is a disease with a wide range that causes great damage to wheat. The traditional method of manually identifying wheat yellow rust is very inefficient. To improve this situation, this study proposed a deep-learning-based method for identifying wheat yellow rust from unmanned aerial vehicle (UAV) images. The method was based on the pyramid scene parsing network (PSPNet) semantic segmentation model to classify healthy wheat, yellow rust wheat, and bare soil in small-scale UAV images, and to investigate the spatial generalization of the model. In addition, it was proposed to use the high-accuracy classification results of traditional algorithms as weak samples for wheat yellow rust identification. The recognition accuracy of the PSPNet model in this study reached 98%. On this basis, this study used the trained semantic segmentation model to recognize another wheat field. The results showed that the method had certain generalization ability, and its accuracy reached 98%. In addition, the high-accuracy classification result of a support vector machine was used as a weak label by weak supervision, which better solved the labeling problem of large-size images, and the final recognition accuracy reached 94%. Therefore, the present study method facilitated timely control measures to reduce economic losses.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

scholarly journals Identifying the Branch of Kiwifruit Based on Unmanned Aerial Vehicle (UAV) Images Using Deep Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4442
Author(s):  
Zijie Niu ◽  
Juntao Deng ◽  
Xu Zhang ◽  
Jun Zhang ◽  
Shijia Pan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Semantic Segmentation ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Distribution Maps ◽  
Time Operation ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Segmentation Image

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

Power information network intrusion detection based on deep learning and cloud computing

2021 ◽  
pp. 1-9
Author(s):  
Xiangbing Zhao ◽  
Jianhui Zhou
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Intrusion Detection ◽  
High Speed ◽  
Information Networks ◽  
Network Intrusion Detection ◽  
Information Network ◽  
Advantages And Disadvantages ◽  
Network Intrusion ◽  
Capture Mechanism

With the advent of the computer network era, people like to think in deeper ways and methods. In addition, the power information network is facing the problem of information leakage. The research of power information network intrusion detection is helpful to prevent the intrusion and attack of bad factors, ensure the safety of information, and protect state secrets and personal privacy. In this paper, through the NRIDS model and network data analysis method, based on deep learning and cloud computing, the demand analysis of the real-time intrusion detection system for the power information network is carried out. The advantages and disadvantages of this kind of message capture mechanism are compared, and then a high-speed article capture mechanism is designed based on the DPDK research. Since cloud computing and power information networks are the most commonly used tools and ways for us to obtain information in our daily lives, our lives will be difficult to carry out without cloud computing and power information networks, so we must do a good job to ensure the security of network information network intrusion detection and defense measures.

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

scholarly journals Detecting the pulmonary trunk in CT scout views using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aydin Demircioğlu ◽  
Magdalena Charis Stein ◽  
Moon-Sung Kim ◽  
Henrike Geske ◽  
Anton S. Quinsten ◽  
...  
Keyword(s):  
Deep Learning ◽  
Validation Cohort ◽  
High Accuracy ◽  
Pulmonary Trunk ◽  
Reference Standard ◽  
Bolus Tracking ◽  
Wilcoxon Rank Sum Test ◽  
Scout View ◽  
Axial Ct ◽  
Anterior Posterior

AbstractFor CT pulmonary angiograms, a scout view obtained in anterior–posterior projection is usually used for planning. For bolus tracking the radiographer manually locates a position in the CT scout view where the pulmonary trunk will be visible in an axial CT pre-scan. We automate the task of localizing the pulmonary trunk in CT scout views by deep learning methods. In 620 eligible CT scout views of 563 patients between March 2003 and February 2020 the region of the pulmonary trunk as well as an optimal slice (“reference standard”) for bolus tracking, in which the pulmonary trunk was clearly visible, was annotated and used to train a U-Net predicting the region of the pulmonary trunk in the CT scout view. The networks’ performance was subsequently evaluated on 239 CT scout views from 213 patients and was compared with the annotations of three radiographers. The network was able to localize the region of the pulmonary trunk with high accuracy, yielding an accuracy of 97.5% of localizing a slice in the region of the pulmonary trunk on the validation cohort. On average, the selected position had a distance of 5.3 mm from the reference standard. Compared to radiographers, using a non-inferiority test (one-sided, paired Wilcoxon rank-sum test) the network performed as well as each radiographer (P < 0.001 in all cases). Automated localization of the region of the pulmonary trunk in CT scout views is possible with high accuracy and is non-inferior to three radiographers.

scholarly journals Financial Information Asymmetry: Using Deep Learning Algorithms to Predict Financial Distress

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 443
Author(s):  
Chyan-long Jan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Information Asymmetry ◽  
Error Rate ◽  
Financial Distress ◽  
Prediction Models ◽  
High Accuracy ◽  
Financial Information ◽  
Financial Distress Prediction ◽  
Distress Prediction

Because of the financial information asymmetry, the stakeholders usually do not know a company’s real financial condition until financial distress occurs. Financial distress not only influences a company’s operational sustainability and damages the rights and interests of its stakeholders, it may also harm the national economy and society; hence, it is very important to build high-accuracy financial distress prediction models. The purpose of this study is to build high-accuracy and effective financial distress prediction models by two representative deep learning algorithms: Deep neural networks (DNN) and convolutional neural networks (CNN). In addition, important variables are selected by the chi-squared automatic interaction detector (CHAID). In this study, the data of Taiwan’s listed and OTC sample companies are taken from the Taiwan Economic Journal (TEJ) database during the period from 2000 to 2019, including 86 companies in financial distress and 258 not in financial distress, for a total of 344 companies. According to the empirical results, with the important variables selected by CHAID and modeling by CNN, the CHAID-CNN model has the highest financial distress prediction accuracy rate of 94.23%, and the lowest type I error rate and type II error rate, which are 0.96% and 4.81%, respectively.

scholarly journals Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2595
Author(s):  
Balakrishnan Ramalingam ◽  
Abdullah Aamir Hayat ◽  
Mohan Rajesh Elara ◽  
Braulio Félix Gómez ◽  
Lim Yi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
High Accuracy ◽  
Experimental Results ◽  
Mobile Mapping ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Reconfigurable Robot ◽  
Nvidia Gpu ◽  
Inspection Task

The pavement inspection task, which mainly includes crack and garbage detection, is essential and carried out frequently. The human-based or dedicated system approach for inspection can be easily carried out by integrating with the pavement sweeping machines. This work proposes a deep learning-based pavement inspection framework for self-reconfigurable robot named Panthera. Semantic segmentation framework SegNet was adopted to segment the pavement region from other objects. Deep Convolutional Neural Network (DCNN) based object detection is used to detect and localize pavement defects and garbage. Furthermore, Mobile Mapping System (MMS) was adopted for the geotagging of the defects. The proposed system was implemented and tested with the Panthera robot having NVIDIA GPU cards. The experimental results showed that the proposed technique identifies the pavement defects and litters or garbage detection with high accuracy. The experimental results on the crack and garbage detection are presented. It is found that the proposed technique is suitable for deployment in real-time for garbage detection and, eventually, sweeping or cleaning tasks.

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