{"id":{"repo_id":"vilnius","oai_identifier":"oai:vu.lt:elaba:146240808"},"canonical_url":"https://search.dev.ndltd.org/etd/vilnius/oai:vu.lt:elaba:146240808","repository":{"repo_id":"vilnius","name":"Vilnius University","base_url":"https://epublications.vu.lt/oai"},"display":{"title":"Skraidančių mikro objektų sekimas /","abstract":"In this work author examined maliciously used unmanned aerial vehicle problem. Proposed solution - create an automated system capable of recognising and aiming at various drones by using convolutional neural network. It was decided to choose three different models - Faster R-CNN, YOLOv4 and YOLOv5. These were then trained with the same dataset for drone recognition and compared by the selected metrics - time it took to train them, mAP and recall. Judging by these metrics YOLOv5 achieved the best results and therefore was chosen for the system's prototype. A physical prototype was then constructed by using two servo motors, micro controller and camera. Prototype is able to recognise and aim at the recognised objects with the laser, however it is not very practical. It lacks mobility, sturdiness and accuracy.","abstract_html":"In this work author examined maliciously used unmanned aerial vehicle problem. Proposed solution - create an automated system capable of recognising and aiming at various drones by using convolutional neural network. It was decided to choose three different models - Faster R-CNN, YOLOv4 and YOLOv5. These were then trained with the same dataset for drone recognition and compared by the selected metrics - time it took to train them, mAP and recall. Judging by these metrics YOLOv5 achieved the best results and therefore was chosen for the system's prototype. A physical prototype was then constructed by using two servo motors, micro controller and camera. Prototype is able to recognise and aim at the recognised objects with the laser, however it is not very practical. It lacks mobility, sturdiness and accuracy.","abstract_has_math":false,"creators":["Balandis, Deividas,"],"institution":"Institutional Repository of Vilnius University","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022","date_published":"2022","updated_at":"2026-07-24T05:55:52Z","subjects":[],"languages":["lit"],"rights":["info:eu-repo/semantics/openAccess"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://repository.vu.lt/VU:ELABAETD146240808&prefLang=en_US","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Balandis, Deividas,"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022"]},{"key":"dc:publisher","label":"Institution","values":["Institutional Repository of Vilnius University"]},{"key":"dc:relation","label":"Dc Relation","values":["https://epublications.vu.lt/object/elaba:146240808/146240808.pdf"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/bachelorThesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["lit"]},{"key":"dc:rights","label":"Dc Rights","values":["info:eu-repo/semantics/openAccess"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://repository.vu.lt/VU:ELABAETD146240808&prefLang=en_US"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this work author examined maliciously used unmanned aerial vehicle problem. Proposed solution - create an automated system capable of recognising and aiming at various drones by using convolutional neural network. It was decided to choose three different models - Faster R-CNN, YOLOv4 and YOLOv5. These were then trained with the same dataset for drone recognition and compared by the selected metrics - time it took to train them, mAP and recall. Judging by these metrics YOLOv5 achieved the best results and therefore was chosen for the system's prototype. A physical prototype was then constructed by using two servo motors, micro controller and camera. Prototype is able to recognise and aim at the recognised objects with the laser, however it is not very practical. It lacks mobility, sturdiness and accuracy."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Skraidančių mikro objektų sekimas /","Tracking flying micro objects for destruction."]}]}],"canonical_facts":{"dc:creator":["Balandis, Deividas,"],"dc:date":["2022"],"dc:description":["In this work author examined maliciously used unmanned aerial vehicle problem. Proposed solution - create an automated system capable of recognising and aiming at various drones by using convolutional neural network. It was decided to choose three different models - Faster R-CNN, YOLOv4 and YOLOv5. These were then trained with the same dataset for drone recognition and compared by the selected metrics - time it took to train them, mAP and recall. Judging by these metrics YOLOv5 achieved the best results and therefore was chosen for the system's prototype. A physical prototype was then constructed by using two servo motors, micro controller and camera. Prototype is able to recognise and aim at the recognised objects with the laser, however it is not very practical. It lacks mobility, sturdiness and accuracy."],"dc:format":["application/pdf"],"dc:identifier":["https://repository.vu.lt/VU:ELABAETD146240808&prefLang=en_US"],"dc:language":["lit"],"dc:publisher":["Institutional Repository of Vilnius University"],"dc:relation":["https://epublications.vu.lt/object/elaba:146240808/146240808.pdf"],"dc:rights":["info:eu-repo/semantics/openAccess"],"dc:title":["Skraidančių mikro objektų sekimas /","Tracking flying micro objects for destruction."],"dc:type":["info:eu-repo/semantics/bachelorThesis"]},"updated_at":"2026-07-24T05:55:52Z"}