The MPU RX-4 Project: Design, Electronics, and Software Development of a Geofence Protection System for a Fixed-Wing VTOL UAV

Tsintotas, Konstantinos A.; Bampis, Loukas; Taitzoglou, Anastasios; Kansizoglou, Ioannis; Kaparos, Pavlos; Bliamis, Chris; Yakinthos, K.; Γαστεράτος, Αντώνιος

doi:10.1109/tim.2022.3225020

Cited by 10 publications

(3 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using risk data stored on the server, the client sets up a geofence around the potentially hazardous location and tracks when the user enters and leaves the fence. This allows the system to send timely alerts and recommendations to individual users who may be at risk [5].…”

Section: )mentioning

confidence: 99%

NDVI prediction using Machine learning after Geofencing on satellite data of Sugarcane crop

Mansi Kambli

2024

jes

View full text Add to dashboard Cite

The manual fencing of the crop is too tedious and time-consuming for the farmers. The farmers have to physically see that they are not crossing the boundaries and usually, farmers fight related to the plots. In this paper focus is on the satellite data imagery of Satara district and if geofencing is done for sugarcane crops then monitoring the crop is comparatively easy and analysis can be further done related to sugarcane plots. Geofencing helps then to have the precise data of the farmer and boundaries can be detected to avoid hassles. The Cane plots that have been geofenced can then be labeled according to the plots of the farmers, thus streamlining th classification process. The digitization of the farmer plots is used in this paper and converted into shape files by using GIS which helps to do the further analysis of the crop. Further after doing the pre-processing by using GIS, the Normalized Difference Vegetation Index (NDVI) is predicted prior using Machine learning technique in python. The NDVI actual and predicted for one life cycle of sugarcane is shown in the paper. The NDVI predicted values can be helpful for sustainable agriculture of sugarcane crop in terms of disease detection, cane classification and prediction also. The Machine learning algorithms can be applied further and the geofencing can be done district wise in future scope along with the vegetation indices.

show abstract

Section: )mentioning

confidence: 99%

NDVI prediction using Machine learning after Geofencing on satellite data of Sugarcane crop

Mansi Kambli

2024

jes

View full text Add to dashboard Cite

show abstract

“…The ease in take-off and landing that the quadrotor layout exhibits, renders it the most suitable for our application [24]. The model used in the Gazebo simulation was the one presented in [25], [26].…”

Section: A Experimental Setupmentioning

confidence: 99%

Embedded light-weight approach for safe landing in populated areas

Mitroudas¹,

Balaska²,

Psomoulis³

et al. 2023

Preprint

View full text Add to dashboard Cite

Landing safety is a challenge heavily engaging the research community recently, due to the increasing interest in applications availed by aerial vehicles. In this paper, we propose a landing safety pipeline based on state of the art object detectors and OctoMap. First, a point cloud of surface obstacles is generated, which is then inserted in an OctoMap. The unoccupied areas are identified, thus resulting to a list of safe landing points. Due to the low inference time achieved by state of the art object detectors and the efficient point cloud manipulation using OctoMap, it is feasible for our approach to deploy on low-weight embedded systems. The proposed pipeline has been evaluated in many simulation scenarios, varying in people density, number, and movement. Simulations were executed with an Nvidia Jetson Nano in the loop to confirm the pipeline's performance and robustness in a low computing power hardware. The experiments yielded promising results with a 95% success rate.

show abstract

“…Even if this can be considered a simple task, it has puzzled computer vision scholars for several decades [2][3][4]. Throughout this period, human action recognition has been widely adopted by various scientific fields, such as human-machine interaction [5,6], medical assistive technologies [7][8][9][10], surveillance systems [11,12], sports analysis [13], and human-robot interaction [14,15]. Similarly, it assists in path planning for tasks like social collision avoidance and route optimization [16] in autonomous navigation [17].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Performance of Mobile-Convolutional Neural Networks for Spatial and Temporal Human Action Recognition Analysis

Moutsis,

Tsintotas,

Kansizoglou

et al. 2023

Robotics

View full text Add to dashboard Cite

Human action recognition is a computer vision task that identifies how a person or a group acts on a video sequence. Various methods that rely on deep-learning techniques, such as two- or three-dimensional convolutional neural networks (2D-CNNs, 3D-CNNs), recurrent neural networks (RNNs), and vision transformers (ViT), have been proposed to address this problem over the years. Motivated by the fact that most of the used CNNs in human action recognition present high complexity, and the necessity of implementations on mobile platforms that are characterized by restricted computational resources, in this article, we conduct an extensive evaluation protocol over the performance metrics of five lightweight architectures. In particular, we examine how these mobile-oriented CNNs (viz., ShuffleNet-v2, EfficientNet-b0, MobileNet-v3, and GhostNet) execute in spatial analysis compared to a recent tiny ViT, namely EVA-02-Ti, and a higher computational model, ResNet-50. Our models, previously trained on ImageNet and BU101, are measured for their classification accuracy on HMDB51, UCF101, and six classes of the NTU dataset. The average and max scores, as well as the voting approaches, are generated through three and fifteen RGB frames of each video, while two different rates for the dropout layers were assessed during the training. Last, a temporal analysis via multiple types of RNNs that employ features extracted by the trained networks is examined. Our results reveal that EfficientNet-b0 and EVA-02-Ti surpass the other mobile-CNNs, achieving comparable or superior performance to ResNet-50.

show abstract

The MPU RX-4 Project: Design, Electronics, and Software Development of a Geofence Protection System for a Fixed-Wing VTOL UAV

Cited by 10 publications

References 99 publications

NDVI prediction using Machine learning after Geofencing on satellite data of Sugarcane crop

NDVI prediction using Machine learning after Geofencing on satellite data of Sugarcane crop

Embedded light-weight approach for safe landing in populated areas

Evaluating the Performance of Mobile-Convolutional Neural Networks for Spatial and Temporal Human Action Recognition Analysis

Contact Info

Product

Resources

About