prikaz prve stranice dokumenta Detekcija ljudi na slikama iz zraka za potrebe misija potrage i spašavanja upotrebom metoda dubokog učenja
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doctoral thesis
Detekcija ljudi na slikama iz zraka za potrebe misija potrage i spašavanja upotrebom metoda dubokog učenja
2019. urn:nbn:hr:179:298735
Marušić, Željko
University of Split
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture
Department of Electronics and Computing

Cite this document

Marušić, Ž. (2019). Detekcija ljudi na slikama iz zraka za potrebe misija potrage i spašavanja upotrebom metoda dubokog učenja (Doctoral thesis). Split: University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:179:298735

Marušić, Željko. "Detekcija ljudi na slikama iz zraka za potrebe misija potrage i spašavanja upotrebom metoda dubokog učenja." Doctoral thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, 2019. https://urn.nsk.hr/urn:nbn:hr:179:298735

Marušić, Željko. "Detekcija ljudi na slikama iz zraka za potrebe misija potrage i spašavanja upotrebom metoda dubokog učenja." Doctoral thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, 2019. https://urn.nsk.hr/urn:nbn:hr:179:298735

Marušić, Ž. (2019). 'Detekcija ljudi na slikama iz zraka za potrebe misija potrage i spašavanja upotrebom metoda dubokog učenja', Doctoral thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, accessed 14 November 2024, https://urn.nsk.hr/urn:nbn:hr:179:298735

Marušić Ž. Detekcija ljudi na slikama iz zraka za potrebe misija potrage i spašavanja upotrebom metoda dubokog učenja [Doctoral thesis]. Split: University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture; 2019 [cited 2024 November 14] Available at: https://urn.nsk.hr/urn:nbn:hr:179:298735

Ž. Marušić, "Detekcija ljudi na slikama iz zraka za potrebe misija potrage i spašavanja upotrebom metoda dubokog učenja", Doctoral thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:179:298735

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Metadata
TitleDetekcija ljudi na slikama iz zraka za potrebe misija potrage i spašavanja upotrebom metoda dubokog učenja
Title (english)Deep Learning Approach in Human Detection on Aerial images for Search and Rescue Missions
AuthorŽeljko Marušić https://orcid.org/0000-0002-1729-6659
MentorSven Gotovac (mentor)
Committee memberVladan Papić (predsjednik povjerenstva)
Committee memberSven Gotovac (član povjerenstva)
Committee memberViktor Sučić (član povjerenstva)
Committee memberSven Lončarić (član povjerenstva)
Committee memberTea Marasović (član povjerenstva)
GranterUniversity of Split
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture
(Department of Electronics and Computing)
Split
Defense date and country2019-10-31, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Artificial Intelligence
Universal decimal classification
(UDC)		621.3 - Electrical engineering
Abstract
U ovom radu predlaže se novi pristup u detekciji osoba na zračnim slikama prikupljenima s bespilotnih letjelica u misijama potrage i spašavanja u mediteranskim i submediteranskim krajolicima. Detekcija ljudi u slikama visoke prostorne razlučivosti uključuje objekte koji su relativno mali i često teško razaznatljivi u okolišu, stoga je takvo otkrivanje izazovan i zahtjevan zadatak. Predložena metoda počinje smanjivanjem prostora za pretraživanje kroz vizualni algoritam pažnje koji otkriva istaknute ili većinu istaknutih segmenata na slici. Kako bi se smanjio broj nevažnih istaknutih regija, odabrane su one regije koje najvjerojatnije sadrže osobu upotrebom unaprijed obučene i fino podešene konvolucijske neuronske mreže (CNN). Stvorena je posebna baza slika pod nazivom HERIDAL u svrhu treniranja i testiranja predloženog modela. Za akviziciju slika korištene su optičke kamere visoke rezolucije instalirane na bespilotnim letjelicama (UAV). Letovi su se većinom izvodili u relativno niskom letu na neurbanim područjima u prirodi. Za potrebe treniranja prikupljeno je više od 68.750 manjih isječaka odnosno segmenata, a u svrhu testiranje prikupljeno je 500 označenih slika u punoj veličini. Predložena metoda postigla je stopu detekcije od 88,9% i preciznosti od 34,8% čime je pokazana veća učinkovitost od IPSAR sustava kojim se trenutno koriste timovi za potragu i spašavanje u Hrvatskoj. Baza slika HERIDAL je korištena za treniranje i testiranje suvremenog generičkog sustava detekcije Faster R-CNN koji je postigao usporedive, ali ipak nešto lošije rezultate od predložene metode.
Abstract (english)
In this paper, we propose a novel approach to person detection in UAV aerial images for search and rescue tasks in Mediterranean and Sub-Mediterranean landscapes. Person detection in very high spatial resolution images involves target objects that are relatively small and often camouflaged within the environment; thus, such detection is a challenging and demanding task. The proposed method starts by reducing the search space through a visual attention algorithm that detects the salient or most prominent segments in the image. To reduce the number of non-relevant salient regions, we selected those regions most likely to contain a person using pre-trained and fine-tuned convolutional neural networks (CNNs) for detection. We established a special database called HERIDAL to train and test our model. This database was compiled for training purposes, and it contains over 68,750 image patches of wilderness acquired from an aerial perspective as well as approximately 500 labelled full-size real-world images intended for testing purposes. The proposed method achieved a detection rate of 88.9% and a precision of 34.8%, which demonstrates better effectiveness than the system currently used by Croatian Mountain search and rescue (SAR) teams (IPSAR), which is based on mean-shift segmentation. We also used the HERIDAL database to train and test a state-of-the-art region proposal network, Faster R-CNN, which achieved comparable but slightly worse results than those of our proposed method.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:179:298735
Promotion2021
Study programmeTitle: Electrical Engineering and Information Technology
Study programme type: university
Study level: postgraduate
Academic / professional title: doktor/doktorica znanosti, područje tehničkih znanosti, polje elektrotehnika (doktor/doktorica znanosti, područje tehničkih znanosti, polje elektrotehnika)
Type of resourceText
Extent146 str.
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2021-10-07 09:48:06