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Applying deep learning to real-time UAV-based forest monitoring: Leveraging multi-sensor imagery for improved results

2024-07Journal article Restricted access
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dc.contributor.authorMarques, Tomás
dc.contributor.authorCarreira, Samuel
dc.contributor.authorMiragaia, Rolando
dc.contributor.authorRamos, João
dc.contributor.authorPereira, António
dc.date.accessioned2024-07-26T16:20:25Z
dc.date.available2024-07-26T16:20:25Z
dc.date.issued2024-07
dc.date.updated2024-07-26T10:21:43Z
dc.descriptionAcknowledgments: This work was financed by national funds through the Portuguese Foundation for Science and Technology - FCT, under the Project ‘‘DBoidS - Digital twin Boids fire prevention System’’ Ref. PTDC/CCICOM/2416/2021pt_PT
dc.descriptionPublisher Policy (Published Version): Institutional Repository - This pathway has an Open Access fee associated with it.pt_PT
dc.description.abstractRising global fire incidents necessitate effective solutions, with forest surveillance emerging as a crucial strategy. This paper proposes a complete solution using technology that integrates visible and infrared spectrum images through Unmanned Aerial Vehicles (UAVs) for enhanced detection of people and vehicles in forest environments. Unlike existing computer vision models relying on single-sensor imagery, this approach overcomes limitations posed by limited spectrum coverage, particularly addressing challenges in low-light conditions, fog, or smoke. The developed 4-channel model uses both types of images to take advantage of the strengths of each one simultaneously. This article presents the development and implementation of a solution for forest monitoring ranging from the transmission of images captured by a UAV to their analysis with an object detection model without human intervention. This model consists of a new version of the YOLOv5 (You Only Look Once) architecture. After the model analyzes the images, the results can be observed on a web platform on any device, anywhere in the world. For the model training, a dataset with thermal and visible images from the aerial perspective was captured with a UAV. From the development of this proposal, a new 4- channel model was created, presenting a substantial increase in precision and mAP (Mean Average Precision) metrics compared to traditional SOTA (state-of-the-art) models that only make use of red, green, and blue (RGB) images. Allied with the increase in precision, we confirmed the hypothesis that our model would perform better in conditions unfavorable to RGB images, identifying objects in situations with low light and reduced visibility with partial occlusions. With the model’s training using our dataset, we observed a significant increase in the model’s performance for images in the aerial perspective. This study introduces a modular system architecture featuring key modules: multisensor image capture, transmission, processing, analysis, and results presentation. Powered by an innovative object detection deep-learning model, these components collaborate to enable real-time, efficient, and distributed forest monitoring across diverse environments.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMarques, T., Carreira, S., Miragaia, R., Ramos, J., & Pereira, A. (2024). Applying deep learning to real-time UAV-based forest monitoring: Leveraging multi-sensor imagery for improved results. Expert Systems with Applications, 245, 123107. https://doi.org/10.1016/j.eswa.2023.123107pt_PT
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2023.123107pt_PT
dc.identifier.eissn1873-6793
dc.identifier.issn0957-4174
dc.identifier.slugcv-prod-3507409
dc.identifier.urihttp://hdl.handle.net/10400.8/9869
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0957417423036114pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectDeep learningpt_PT
dc.subjectComputer visionpt_PT
dc.subjectImage fusionpt_PT
dc.subjectObject detectionpt_PT
dc.subjectReal-timept_PT
dc.subjectUnmanned Aerial Vehiclept_PT
dc.titleApplying deep learning to real-time UAV-based forest monitoring: Leveraging multi-sensor imagery for improved resultspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-COM%2F2416%2F2021/PT
oaire.citation.startPage123107pt_PT
oaire.citation.titleExpert Systems with Applicationspt_PT
oaire.citation.volume245pt_PT
oaire.fundingStream3599-PPCDT
person.familyNameMarques
person.familyNameCarreira
person.familyNameMiragaia
person.familyNameRamos
person.familyNamePereira
person.givenNameTomás
person.givenNameSamuel
person.givenNameRolando
person.givenNameJoão
person.givenNameAntónio
person.identifier.ciencia-id491A-67A5-46F9
person.identifier.ciencia-id531E-41C9-CE25
person.identifier.ciencia-idC712-E02E-0ED2
person.identifier.ciencia-id8417-9F61-D162
person.identifier.ciencia-idE215-4F0F-33EC
person.identifier.orcid0009-0002-9783-9752
person.identifier.orcid0009-0001-2117-7994
person.identifier.orcid0000-0003-4213-9302
person.identifier.orcid0000-0001-5361-9809
person.identifier.orcid0000-0001-5062-1241
person.identifier.ridGLS-3615-2022
person.identifier.ridM-6163-2013
person.identifier.scopus-author-id26422369700
person.identifier.scopus-author-id7402230199
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.cv.cienciaidC712-E02E-0ED2 | Rolando Lúcio Germano Miragaia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
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