prikaz prve stranice dokumenta Suvremene metode detekcije objekata
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undergraduate thesis
Suvremene metode detekcije objekata
2024. urn:nbn:hr:168:983331
Vivoda, Anton
University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

Vivoda, A. (2024). Suvremene metode detekcije objekata (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:983331

Vivoda, Anton. "Suvremene metode detekcije objekata." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:983331

Vivoda, Anton. "Suvremene metode detekcije objekata." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:983331

Vivoda, A. (2024). 'Suvremene metode detekcije objekata', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:983331

Vivoda A. Suvremene metode detekcije objekata [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2024 [cited 2024 December 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:983331

A. Vivoda, "Suvremene metode detekcije objekata", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:168:983331

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Metadata
TitleSuvremene metode detekcije objekata
Title (english)State-of-the-art Object Detection Methods
AuthorAnton Vivoda
MentorMarin Šilić (mentor)
Committee memberMarin Šilić (predsjednik povjerenstva)
Committee memberIvan Sabolić (član povjerenstva)
Committee memberIva Sović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2024-09-10, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovaj rad istražuje evoluciju detekcije objekata, s fokusom na jednoprolazne metode i dvoprolazne metode. Rad ističe razvoj ključnih algoritama kao što su R-CNN, Fast R-CNN, YOLO i SSD , te analizira njihove prednosti, ograničenja i primjene. Također se govori o važnosti javnih skupova podataka kao što su COCO, PASCAL VOC i ImageNet u poticanju napretka u ovom području. Pored toga, analizira se i uspoređuje rezultat YOLOv1 i R-CNN modela na malom skupu podataka.
Abstract (english)
This paper explores the evolution of object detection, focusing on single-pass and two-pass methods. It highlights the development of key algorithms such as R-CNN, Fast R-CNN, YOLO, and SSD, and analyzes their advantages, limitations, and applications. The importance of public datasets like COCO, PASCAL VOC, and ImageNet in driving progress in this field is also discussed. Additionally, the paper analyzes and compares the performance of the YOLOv1 and R-CNN models on a small dataset.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:983331
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2024-10-12 22:26:26