Evidence map›Paper›PMID 35161639›Full record

ArticleSensors (Basel, Switzerland)2022

A Path Planning Method with Perception Optimization Based on Sky Scanning for UAVs.

Songhe Yuan, Kaoru Ota, Mianxiong Dong, Jianghai Zhao

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Songhe YuanInstitute of Advanced Manufacturing Technology, HeFei Institutes of Physical Science, Chinese Academy of Sciences, Changzhou 213164, China.ORCID 0000-0002-8131-5009
Kaoru OtaDepartment of Information and Electronic Engineering, Muroran Institute of Technology, Muroran 050-8585, Hokkaido, Japan.
Mianxiong DongDepartment of Information and Electronic Engineering, Muroran Institute of Technology, Muroran 050-8585, Hokkaido, Japan.
Jianghai ZhaoHeFei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unmanned aerial vehicles (UAVs) are frequently adopted in disaster management. The vision they provide is extremely valuable for rescuers. However, they face severe problems in their stability in actual disaster scenarios, as the images captured by the on-board sensors cannot consistently give enough information for deep learning models to make accurate decisions. In many cases, UAVs have to capture multiple images from different views to output final recognition results. In this paper, we desire to formulate the fly path task for UAVs, considering the actual perception needs. A convolutional neural networks (CNNs) model is proposed to detect and localize the objects, such as the buildings, as well as an optimization method to find the optimal flying path to accurately recognize as many objects as possible with a minimum time cost. The simulation results demonstrate that the proposed method is effective and efficient, and can address the actual scene understanding and path planning problems for UAVs in the real world well.

Indexed as

DisastersUnmanned Aerial DevicesComputer SimulationNeural Networks, ComputerPerceptionpath planningscene understandingunmanned aerial vehicle (UAV)

Identifiers

PMID35161639
PMCPMC8839164

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.