Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines

Baur, Jasper; Steinberg, Gabriel; Nikulin, Alex; Chiu, Kenneth; Smet, Timothy S. de

doi:10.3390/rs12050859

Cited by 36 publications

(18 citation statements)

References 35 publications

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“…However, the use of high spatial resolution data collected from RPAS and aerial platforms can provide additional information on plastic items like geometry, which may aid in separating plastics from other non-synthetic targets like whitecaps [19]. There is evidence that some low-cost plug-and-play multispectral devices mounted on RPAS are capable of separating plastic objects from terrestrial backgrounds using VNIR and thermal IR data [55].…”

Section: Discussionmentioning

confidence: 99%

Towards the Spectral Mapping of Plastic Debris on Beaches

et al. 2021

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Floating and washed ashore marine plastic debris (MPD) is a growing environmental challenge. It has become evident that secluded locations including the Arctic, Antarctic, and remote islands are being impacted by plastic pollution generated thousands of kilometers away. Optical remote sensing of MPD is an emerging field that can aid in monitoring remote environments where in-person observation and data collection is not always feasible. Here we evaluate MPD spectral features in the visible to shortwave infrared regions for detecting varying quantities of MPD that have accumulated on beaches using a spectroradiometer. Measurements were taken from a range of in situ MPD accumulations ranging from 0.08% to 7.94% surface coverage. Our results suggest that spectral absorption features at 1215 nm and 1732 nm are useful for detecting varying abundance levels of MPD in a complex natural environment, however other absorption features at 931 nm, 1045 nm and 2046 nm could not detect in situ MPD. The reflectance of some in situ MPD accumulations was statistically different from samples that only contained organic debris and sand between 1.56% and 7.94% surface cover; however other samples with similar surface cover did not have reflectance that was statistically different from samples containing no MPD. Despite MPD being detectable against a background of sand and organic beach debris, a clear relationship between the surface cover of MPD and the strength of key absorption features could not be established. Additional research is needed to advance our understanding of the factors, such as type of MPD assemblage, that contribute to the bulk reflectance of MPD contaminated landscapes.

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Section: Discussionmentioning

confidence: 99%

Towards the Spectral Mapping of Plastic Debris on Beaches

et al. 2021

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“…Therefore, UAVs can be used for the demining process, as they have the ability to cover a wider area of any terrain in less time compared with unmanned ground vehicles (UGVs) [87] as it can be seen in Figure 11. UAVs can be equipped with various sensors to assist in the process of landmine detection; in [88] the authors used a multispectrum camera and a thermal camera to help in the detection of PFM-1 scatterable plastic landmines by applying a computer vision algorithm to automate the process of detecting and localization. In [89], a was UAV equipped with a ground penetrating radar (GPR) system to detect the landmines.…”

Section: Humanitarian Deminingmentioning

confidence: 99%

A Detailed Survey and Future Directions of Unmanned Aerial Vehicles (UAVs) with Potential Applications

2021

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Recently, unmanned aerial vehicles (UAVs), also known as drones, have gained widespread interest in civilian and military applications, which has led to the development of novel UAVs that can perform various operations. UAVs are aircraft that can fly without the need of a human pilot onboard, meaning they can fly either autonomously or be remotely piloted. They can be equipped with multiple sensors, including cameras, inertial measurement units (IMUs), LiDAR, and GPS, to collect and transmit data in real time. Due to the demand for UAVs in various applications such as precision agriculture, search and rescue, wireless communications, and surveillance, several types of UAVs have been invented with different specifications for their size, weight, range and endurance, engine type, and configuration. Because of this variety, the design process and analysis are based on the type of UAV, with the availability of several control techniques that could be used to improve the flight of the UAV in order to avoid obstacles and potential collisions, as well as find the shortest path to save the battery life with the support of optimization techniques. However, UAVs face several challenges in order to fly smoothly, including collision avoidance, battery life, and intruders. This review paper presents UAVs’ classification, control applications, and future directions in industry and research interest. For the design process, fabrication, and analysis, various control approaches are discussed in detail. Furthermore, the challenges for UAVs, including battery charging, collision avoidance, and security, are also presented and discussed.

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“…Additionally, the authors of [27][28][29][30] proposed UAVs can be deployed to help ground vehicles find communication paths for their message. In addition, the researchers of [31][32][33] proposed that UAV-based remote sensing can apply deep learning techniques to UAV-assisted VANETs. Furthermore, Mukherjee et al,in [34,35], study UAV-assisted MEC and aim to maximize UAV energy efficiency by jointly optimizing the UAV trajectory, the user transmit power, and computation load allocation.…”

Section: Uav-assisted Vanetsmentioning

confidence: 99%

Joint Task Offloading, Resource Allocation, and Security Assurance for Mobile Edge Computing-Enabled UAV-Assisted VANETs

Zhai

Huang

et al. 2021

Remote Sensing

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In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial vehicle (UAV)-assisted vehicular ad hoc network (VANET) architecture, based on which a number of vehicles are served by UAVs equipped with computation resource. Each vehicle has to offload its computing tasks to the proper MEC server on the UAV due to the limited computation ability. To counter the problems above, we first model and analyze the transmission model and the security assurance model from the vehicle to the MEC server on UAV, and the task computation model of the local vehicle and the edge UAV. Then, the vehicle offloading problem is formulated as a multi-objective optimization problem by jointly considering the task offloading, the resource allocation, and the security assurance. For tackling this hard problem, we decouple the multi-objective optimization problem as two subproblems and propose an efficient iterative algorithm to jointly make the MEC selection decision based on the criteria of load balancing and optimize the offloading ratio and the computation resource according to the Lagrangian dual decomposition. Finally, the simulation results demonstrate that our proposed scheme achieves significant performance superiority compared with other schemes in terms of the successful task processing ratio and the task processing delay.

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Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines

Cited by 36 publications

References 35 publications

Towards the Spectral Mapping of Plastic Debris on Beaches

Towards the Spectral Mapping of Plastic Debris on Beaches

A Detailed Survey and Future Directions of Unmanned Aerial Vehicles (UAVs) with Potential Applications

Joint Task Offloading, Resource Allocation, and Security Assurance for Mobile Edge Computing-Enabled UAV-Assisted VANETs

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