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master's thesis
Detekcija kapljica kiše na zaštitnom staklu između scene i kamere
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:653967
Barišić, Ivana
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Barišić, I. (2022). Detekcija kapljica kiše na zaštitnom staklu između scene i kamere (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:653967

Barišić, Ivana. "Detekcija kapljica kiše na zaštitnom staklu između scene i kamere." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:653967

Barišić, Ivana. "Detekcija kapljica kiše na zaštitnom staklu između scene i kamere." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:653967

Barišić, I. (2022). 'Detekcija kapljica kiše na zaštitnom staklu između scene i kamere', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:653967

Barišić I. Detekcija kapljica kiše na zaštitnom staklu između scene i kamere [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 December 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:653967

I. Barišić, "Detekcija kapljica kiše na zaštitnom staklu između scene i kamere", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:653967

Metadata
TitleDetekcija kapljica kiše na zaštitnom staklu između scene i kamere
Title (english)Detection of raindrops on the protective glass between the scene and the camera
AuthorIvana Barišić
MentorSiniša Šegvić (mentor)
Committee memberSiniša Šegvić (predsjednik povjerenstva)
Committee memberMarko Čupić (član povjerenstva)
Committee memberJan Šnajder (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-13, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Raspoznavanje slika važno je područje računalnog vida s mnogim zanimljivim primjenama. Jedna od takvih primjena je i detekcija smanjene vidljivosti uslijed kapljica na zaštitnom staklu kamere. Ovaj rad proučava rješavanje tog problema prikladno oblikovanim konvolucijskim modelima, točnije korištenjem modela s ResNet-18 arhitekturom. Raznim postupcima, poput učenja na isječcima, uvođenjem dilatirane konvolucije te smanjenjem koraka u prvom sloju sažimanja, pokušava se dobiti što bolja sposobnost generalizacije. Fokus je stavljen na prepoznavanja sitnih detalja slike i očuvanje originalne ImageNet semantike mreže. U sklopu rada korištena su dva skupa podataka od kojih su oblikovani podskupovi za učenje, validaciju i testiranje. Kreiran je klasifikacijski algoritam strojnog učenja te su vrednovani naučeni modeli i prikazane su njihove metrike kao i vizualizirana logika zaključivanja modela pomoću gradijenata ulaza.
Abstract (english)
Image recognition is an important area of computer vision with many interesting applications such as the detection of reduced visibility due to drops on the protective glass of the camera. This paper studies the solution to this problem with appropriately designed convolutional models, i.e. using ResNet-18 models. The best possible model was deducted by a combination of various procedures, such as learning on clips, introducing dilated convolution and reducing stride in the first pooling layer. The focus was on recognizing the fine details of the image and preserving the original ImageNet semantics of the network. Subsets for learning, validation and testing were formed from two data sets which were used in the paper. The classification algorithm of machine learning was created and the learned models were evaluated, along with their metrics, as well as the visualized logic of the model inference using input gradients to do so.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:653967
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-14 08:24:22