RESUMO

Identifying plantation lines in aerial images of agricultural landscapes is re-quired for many automatic farming processes. Deep learning-based networks are among the most prominent methods to learn such patterns and extract this type of information from diverse imagery conditions. However, even state-of-the-art methods may stumble in complex plantation patterns. Here, we propose a deep learning approach based on graphs to detect plantation lines in UAV-based RGB imagery, presenting a challenging scenario containing spaced plants. The first module of our method extracts a feature map throughout the backbone, which consists of the initial layers of the VGG16. This feature map is used as an input to the Knowledge Estimation Module (KEM), organized in three concatenated branches for detecting 1) the plant positions, 2) the plantation lines, and 3) the displacement vectors between the plants. A graph modeling is applied considering each plant position on the image as vertices, and edges are formed between two vertices (i.e. plants). Finally, the edge is classified as pertaining to a certain plantation line based on three probabilities (higher than 0.5): i) in visual features obtained from the backbone; ii) a chance that the edge pixels belong to a line, from the KEM step; and iii) an alignment of the displacement vectors with the edge, also from the KEM step. Experiments were conducted initially in corn plantations with different growth stages and patterns with aerial RGB imagery to present the advantages of adopting each module. We assessed the generalization capability in the other two cultures (orange and eucalyptus) datasets. The proposed method was compared against state-of-the-art deep learning methods and achieved superior performance with a significant margin considering all three datasets. This approach is useful in extracting lines with spaced plantation patterns and could be implemented in scenarios where plantation gaps occur, generating lines with few-to-no interruptions.

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Abstract This article reported two clinical cases in which the guided endodontics was used to perform the access to the root canals. The first case presents a 40-year-old female with a history of pain related to the left maxillary canine. After radiographic examination, the presence of severe calcification up to the apical third of the root canal, associated with a periapical radiolucency, was noted. In the second case, an 85-year-old male was referred to our service with pain upon palpation, at the right mandibular first molar. The radiographic images revealed the presence of endodontic treatment and a fiberglass post in the distal root canal, which was associated with extrusion of the filling material and a periapical lesion. The 3D-guides were planned based on cone beam computed tomography and intraoral digital scanning, which were aligned using a specific software. Therefore, implant drills could be guided up to the root canal length required for each case. In the first case, a surgical root canal was created and the patient was free of signs and symptoms after the treatment was completed. In the second case, it was observed that the fiber post was worn by the drill, allowing free access to the filling material. It was possible to perform the endodontic reintervention in a more predictable way and in less time. In both cases, the use of the guided endodontics allowed the preservation of a large part of the dental structure. The procedures were performed faster, without the occurrence of fractures and perforations.

Resumo Este artigo relatou dois casos clínicos em que a endodontia guiada foi utilizada para realizar o acesso aos canais radiculares. O primeiro caso apresenta uma mulher de 40 anos com história de dor relacionada ao canino superior esquerdo. Após exame radiográfico, notou-se a presença de calcificação acentuada até o terço apical do canal radicular, associada a radioluscência periapical. No segundo caso, um homem de 85 anos foi encaminhado ao nosso serviço com dor à palpação no primeiro molar inferior direito. As imagens radiográficas revelaram a presença de tratamento endodôntico e pino de fibra de vidro no canal radicular distal, que estava associado à extrusão do material obturador e lesão periapical. Os guias-3D foram planejados com base em tomografia computadorizada de feixe cônico e escaneamento intraoral digital, os quais foram alinhados por meio de um software específico. Desta forma, brocas de implante puderam ser guiadas até o comprimento necessário do canal radicular para cada caso. No primeiro caso, foi confeccionado um canal radicular cirúrgico e o paciente ficou sem sinais e sintomas após o término do tratamento. No segundo caso, observou-se que o pino de fibra foi desgastado pela broca, permitindo o livre acesso ao material obturador. Foi possível realizar a reintervenção endodôntica de forma mais previsível e em menos tempo. Em ambos os casos, o uso da endodôntica guiada permitiu a preservação de grande parte da estrutura dentária. Os procedimentos foram realizados com maior agilidade, sem a ocorrência de fraturas e perfurações.

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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. 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. The results returned a mean absolute error (MAE) of 0.75 trees and an F1-measure of 86.9%. These results are better than Faster R-CNN and RetinaNet methods considering equal experiment conditions. 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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Forage dry matter is the main source of nutrients in the diet of ruminant animals. Thus, this trait is evaluated in most forage breeding programs with the objective of increasing the yield. Novel solutions combining unmanned aerial vehicles (UAVs) and computer vision are crucial to increase the efficiency of forage breeding programs, to support high-throughput phenotyping (HTP), aiming to estimate parameters correlated to important traits. The main goal of this study was to propose a convolutional neural network (CNN) approach using UAV-RGB imagery to estimate dry matter yield traits in a guineagrass breeding program. For this, an experiment composed of 330 plots of full-sib families and checks conducted at Embrapa Beef Cattle, Brazil, was used. The image dataset was composed of images obtained with an RGB sensor embedded in a Phantom 4 PRO. The traits leaf dry matter yield (LDMY) and total dry matter yield (TDMY) were obtained by conventional agronomic methodology and considered as the ground-truth data. Different CNN architectures were analyzed, such as AlexNet, ResNeXt50, DarkNet53, and two networks proposed recently for related tasks named MaCNN and LF-CNN. Pretrained AlexNet and ResNeXt50 architectures were also studied. Ten-fold cross-validation was used for training and testing the model. Estimates of DMY traits by each CNN architecture were considered as new HTP traits to compare with real traits. Pearson correlation coefficient r between real and HTP traits ranged from 0.62 to 0.79 for LDMY and from 0.60 to 0.76 for TDMY; root square mean error (RSME) ranged from 286.24 to 366.93 kg·ha-1 for LDMY and from 413.07 to 506.56 kg·ha-1 for TDMY. All the CNNs generated heritable HTP traits, except LF-CNN for LDMY and AlexNet for TDMY. Genetic correlations between real and HTP traits were high but varied according to the CNN architecture. HTP trait from ResNeXt50 pretrained achieved the best results for indirect selection regardless of the dry matter trait. This demonstrates that CNNs with remote sensing data are highly promising for HTP for dry matter yield traits in forage breeding programs.

Assuntos

RESUMO

This article reported two clinical cases in which the guided endodontics was used to perform the access to the root canals. The first case presents a 40-year-old female with a history of pain related to the left maxillary canine. After radiographic examination, the presence of severe calcification up to the apical third of the root canal, associated with a periapical radiolucency, was noted. In the second case, an 85-year-old male was referred to our service with pain upon palpation, at the right mandibular first molar. The radiographic images revealed the presence of endodontic treatment and a fiberglass post in the distal root canal, which was associated with extrusion of the filling material and a periapical lesion. The 3D-guides were planned based on cone beam computed tomography and intraoral digital scanning, which were aligned using a specific software. Therefore, implant drills could be guided up to the root canal length required for each case. In the first case, a surgical root canal was created and the patient was free of signs and symptoms after the treatment was completed. In the second case, it was observed that the fiber post was worn by the drill, allowing free access to the filling material. It was possible to perform the endodontic reintervention in a more predictable way and in less time. In both cases, the use of the guided endodontics allowed the preservation of a large part of the dental structure. The procedures were performed faster, without the occurrence of fractures and perforations.

Assuntos

RESUMO

Mapping utility poles using side-view images acquired with car-mounted cameras is a time-consuming task, mainly in larger areas due to the need for street-by-street surveying. Aerial images cover larger areas and can be feasible alternatives although the detection and mapping of the utility poles in urban environments using top-view images is challenging. Thus, we propose the use of Adaptive Training Sample Selection (ATSS) for detecting utility poles in urban areas since it is a novel method and has not yet investigated in remote sensing applications. Here, we compared ATSS with Faster Region-based Convolutional Neural Networks (Faster R-CNN) and Focal Loss for Dense Object Detection (RetinaNet ), currently used in remote sensing applications, to assess the performance of the proposed methodology. We used 99,473 patches of 256 × 256 pixels with ground sample distance (GSD) of 10 cm. The patches were divided into training, validation and test datasets in approximate proportions of 60%, 20% and 20%, respectively. As the utility pole labels are point coordinates and the object detection methods require a bounding box, we assessed the influence of the bounding box size on the ATSS method by varying the dimensions from 30×30 to 70×70 pixels. For the proposal task, our findings show that ATSS is, on average, 5% more accurate than Faster R-CNN and RetinaNet. For a bounding box size of 40×40, we achieved Average Precision with intersection over union of 50% (AP50) of 0.913 for ATSS, 0.875 for Faster R-CNN and 0.874 for RetinaNet. Regarding the influence of the bounding box size on ATSS, our results indicate that the AP50 is about 6.5% higher for 60×60 compared to 30×30. For AP75, this margin reaches 23.1% in favor of the 60×60 bounding box size. In terms of computational costs, all the methods tested remain at the same level, with an average processing time around of 0.048 s per patch. Our findings show that ATSS outperforms other methodologies and is suitable for developing operation tools that can automatically detect and map utility poles.

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Monitoring biomass of forages in experimental plots and livestock farms is a time-consuming, expensive, and biased task. Thus, non-destructive, accurate, precise, and quick phenotyping strategies for biomass yield are needed. To promote high-throughput phenotyping in forages, we propose and evaluate the use of deep learning-based methods and UAV (Unmanned Aerial Vehicle)-based RGB images to estimate the value of biomass yield by different genotypes of the forage grass species Panicum maximum Jacq. Experiments were conducted in the Brazilian Cerrado with 110 genotypes with three replications, totaling 330 plots. Two regression models based on Convolutional Neural Networks (CNNs) named AlexNet and ResNet18 were evaluated, and compared to VGGNet-adopted in previous work in the same thematic for other grass species. The predictions returned by the models reached a correlation of 0.88 and a mean absolute error of 12.98% using AlexNet considering pre-training and data augmentation. This proposal may contribute to forage biomass estimation in breeding populations and livestock areas, as well as to reduce the labor in the field.

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As key-components of the urban-drainage system, storm-drains and manholes are essential to the hydrological modeling of urban basins. Accurately mapping of these objects can help to improve the storm-drain systems for the prevention and mitigation of urban floods. Novel Deep Learning (DL) methods have been proposed to aid the mapping of these urban features. The main aim of this paper is to evaluate the state-of-the-art object detection method RetinaNet to identify storm-drain and manhole in urban areas in street-level RGB images. The experimental assessment was performed using 297 mobile mapping images captured in 2019 in the streets in six regions in Campo Grande city, located in Mato Grosso do Sul state, Brazil. Two configurations of training, validation, and test images were considered. ResNet-50 and ResNet-101 were adopted in the experimental assessment as the two distinct feature extractor networks (i.e., backbones) for the RetinaNet method. The results were compared with the Faster R-CNN method. The results showed a higher detection accuracy when using RetinaNet with ResNet-50. In conclusion, the assessed DL method is adequate to detect storm-drain and manhole from mobile mapping RGB images, outperforming the Faster R-CNN method. The labeled dataset used in this study is available for future research.

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This study proposes and evaluates five deep fully convolutional networks (FCNs) for the semantic segmentation of a single tree species: SegNet, U-Net, FC-DenseNet, and two DeepLabv3+ variants. The performance of the FCN designs is evaluated experimentally in terms of classification accuracy and computational load. We also verify the benefits of fully connected conditional random fields (CRFs) as a post-processing step to improve the segmentation maps. The analysis is conducted on a set of images captured by an RGB camera aboard a UAV flying over an urban area. The dataset also contains a mask that indicates the occurrence of an endangered species called Dipteryx alata Vogel, also known as cumbaru, taken as the species to be identified. The experimental analysis shows the effectiveness of each design and reports average overall accuracy ranging from 88.9% to 96.7%, an F1-score between 87.0% and 96.1%, and IoU from 77.1% to 92.5%. We also realize that CRF consistently improves the performance, but at a high computational cost.

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Detection and classification of tree species from remote sensing data were performed using mainly multispectral and hyperspectral images and Light Detection And Ranging (LiDAR) data. Despite the comparatively lower cost and higher spatial resolution, few studies focused on images captured by Red-Green-Blue (RGB) sensors. Besides, the recent years have witnessed an impressive progress of deep learning methods for object detection. Motivated by this scenario, we proposed and evaluated the usage of Convolutional Neural Network (CNN)-based methods combined with Unmanned Aerial Vehicle (UAV) high spatial resolution RGB imagery for the detection of law protected tree species. Three state-of-the-art object detection methods were evaluated: Faster Region-based Convolutional Neural Network (Faster R-CNN), YOLOv3 and RetinaNet. A dataset was built to assess the selected methods, comprising 392 RBG images captured from August 2018 to February 2019, over a forested urban area in midwest Brazil. The target object is an important tree species threatened by extinction known as Dipteryx alata Vogel (Fabaceae). The experimental analysis delivered average precision around 92% with an associated processing times below 30 miliseconds.

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O efeito da interferência contextual (EIC) é somente observado quando as variações das tarefas requerem diferentes programas motores generalizados (PMGs) (MAGILL & HALL, 1990). Entretanto, alguns estudos encontraram resultados inconsistentes quando verificaram essa hipótese. O presente estudo comparou o desempenho de grupos de prática aleatória e em blocos que variaram PMGs ou parâmetros em uma nova tarefa que exigiu a adaptação a uma nova estrutura do movimento e a um novo valor de parâmetro. Quarenta e oito participantes foram designados aleatoriamente para um dos quatro grupos de prática: aleatório PMG, aleatório parâmetros, em blocos PMG e em blocos parâmetros. A tarefa praticada pelos sujeitos consistiu em transportar três bolas de tênis entre seis recipientes em uma caixa. Os participantesdos grupos PMGs praticaram três diferentes seqüências de movimentos em somente um tempo alvo (2.700 ms) durante a fase de aquisição. Os sujeitos dos grupos parâmetros praticaram uma seqüência de movimentos em três diferentes tempos alvos (2.500 ms; 2.700 ms; 2.900 ms). Nos testes de transferência e retenção da transferência foram requeridos uma nova seqüência e tempo alvo (2.300ms). A análise dos testes indicou um menor nível de erro absoluto para o grupo aleatório parâmetros comparado aos demais grupos. Este resultado não suporta a hipótese de MAGILL e HALL (1990). A variação de parâmetros de forma aleatória levou a uma melhor adaptação em um novo contexto. Uma possível explicação é que este tipo de manipulação da prática criou um nível ótimo de interferência na aprendizagem de uma nova tarefa.

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