Semantic Navigation Mapping from Aerial Multispectral Imagery

Salvado, Ana Beatriz; Mendonca, Ricardo; Lourenço, André; Marques, Francisco; Matos-Carvalho, J. P.; Campos, Luis Miguel; Barata, José

doi:10.1109/isie.2019.8781301

Cited by 14 publications

(5 citation statements)

References 8 publications

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“…ere is always a semantic gap between using low-level features to describe images and perceiving images by humans. e introduction of semantic features into maps [26][27][28][29] makes the description of images closer to the level of human understanding, which can alleviate this problem to a certain extent and improve the robustness of UAV visual relocalization. In [28], the authors indicate that a defined hierarchical structure of semantic information enables improved map reproduction.…”

Section: Introductionmentioning

confidence: 99%

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A UAV Visual Relocalization Method Using Semantic Object Features Based on Internet of Things

Wang

et al. 2022

Wireless Communications and Mobile Computing

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Unmanned Air Vehicle (UAV) has the advantages of high autonomy and strong dynamic deployment capabilities. At the same time, with the rapid development of the Internet of Things (IoT) technology, the construction of the IoT based on UAVs can break away from the traditional single-line communication mode of UAVs and control terminals, which makes the UAVs more intelligent and flexible when performing tasks. When using UAVs to perform IoT tasks, it is necessary to track the UAVs’ position and pose at all times. When the position and pose tracking fails, relocalization is required to restore the current position and pose. Therefore, how to perform UAV relocalization accurately by using visual information has attracted much attention. However, the complex changes in light conditions in the real world have brought huge challenges to the visual relocalization of UAV. Traditional visual relocalization algorithms mostly rely on artificially designed low-level geometric features which are sensitive to light conditions. In this paper, oriented to the UAV-based IoT, a UAV visual relocalization method using semantic object features is proposed. Specifically, the method uses YOLOv3 as the object detection framework to extract the semantic information in the picture and uses the semantic information to construct a topological map as a sparse description of the environment. With prior knowledge of the map, the random walk algorithm is used on the association graphs to match the semantic features and the scenes. Finally, the EPnP algorithm is used to solve the position and pose of the UAV which will be returned to the IoT platform. Simulation results show that the method proposed in this paper can achieve robust real-time UAVs relocalization when the scene lighting conditions change dynamically and provide a guarantee for UAVs to perform IoT tasks.

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Section: Introductionmentioning

confidence: 99%

“…In [28], the authors indicate that a defined hierarchical structure of semantic information enables improved map reproduction. In [29], the authors introduce semantic information into the map to enable the UAVs to complete terrain classification and navigation.…”

Section: Introductionmentioning

confidence: 99%

A UAV Visual Relocalization Method Using Semantic Object Features Based on Internet of Things

Wang

et al. 2022

Wireless Communications and Mobile Computing

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“…On the one hand, artificial intelligence and CNN have been successfully used in several scientific fields, such as fashion [12], localization [13][14][15], and digital health [16]. On the other hand, UAV and artificial intelligence in conjunction have also been successfully applied in PA for crop disease identification [17] or flower classification [18,19]. Nevertheless, and as far as the author's knowledge goes, gap detection, namely, for vineyard crops, has not been considered in the scientific literature.…”

Section: Introductionmentioning

confidence: 99%

Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images

Sulemane

Matos-Carvalho

Pedro³

et al. 2022

Algorithms

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This paper focuses on the gaps that occur inside plantations; these gaps, although not having anything growing in them, still happen to be watered. This action ends up wasting tons of liters of water every year, which translates into financial and environmental losses. To avoid these losses, we suggest early detection. To this end, we analyzed the different available neural networks available with multispectral images. This entailed training each regional and regression-based network five times with five different datasets. Networks based on two possible solutions were chosen: unmanned aerial vehicle (UAV) depletion or post-processing with external software. The results show that the best network for UAV depletion is the Tiny-YOLO (You Only Look Once) version 4-type network, and the best starting weights for Mask-RCNN were from the Tiny-YOLO network version. Although no mean average precision (mAP) of over 70% was achieved, the final trained networks managed to detect mostly gaps, including low-vegetation areas and very small gaps, which had a tendency to be overlooked during the labeling stage.

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“…In addition to the visible band (RGB), there are also bands with other frequencies that are used in terrain classification [58,59]. The cameras that use these bands are usually thermal (flir cameras) and multispectral cameras (for example, Micasense RedEdge), as shown in Figure 26.…”

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confidence: 99%

“…From the images obtained by the cameras presented in Figure 26, the authors, after aligning the lenses from the red band, used five multispectral indices (NDVI, ENDVI RDVI, MSAVI, and SR) to classify four different terrain types: water, vegetation, sand, and rocks [59]. However, despite the good accuracy of the system proposed by the authors, detecting various types of terrain when the height is higher than 60 m can mean a loss of resolution when classified.…”

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confidence: 99%

Particle Swarm Optimization Embedded in UAV as a Method of Territory-Monitoring Efficiency Improvement

et al. 2022

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Unmanned aerial vehicles have large prospects for organizing territory monitoring. To integrate them into this sphere, it is necessary to improve their high functionality and safety. Computer vision is one of the vital monitoring aspects. In this paper, we developed and validated a methodology for terrain classification. The overall classification procedure consists of the following steps: (1) pre-processing, (2) feature extraction, and (3) classification. For the pre-processing stage, a clustering method based on particle swarm optimization was elaborated, which helps to extract object patterns from the image. Feature extraction is conducted via Gray-Level Co-Occurrence Matrix calculation, and the output of the matrix is turned into the input for a feed-forward neural network classification stage. The developed computer vision system showed 88.7% accuracy on the selected test set. These results can provide high quality territory monitoring; prospectively, we plan to establish a self-positioning system based on computer vision.

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Semantic Navigation Mapping from Aerial Multispectral Imagery

Cited by 14 publications

References 8 publications

A UAV Visual Relocalization Method Using Semantic Object Features Based on Internet of Things

A UAV Visual Relocalization Method Using Semantic Object Features Based on Internet of Things

Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images

Particle Swarm Optimization Embedded in UAV as a Method of Territory-Monitoring Efficiency Improvement

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