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Object Detection and Instance Segmentation of Cables
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Objektdetektion och instanssegmentering av kablar (Swedish)
Abstract [en]

This thesis introduces an innovative method to detect and do segmentation of cables for visual inspection. Cables lack significant features and fixed structures, which are difficult to capture with a cluttered background. This method is based on cable color and cable width in a specific scenario. It takes a splitand-merge approach to do detection and segmentation. This method can be used to inspect the status of cables on radio towers for maintenance and damage assessment by analyzing photos captured by unmanned aerial vehicles (UAV). This method to detect cables may also be beneficial to fields of navigation of UAV and navigation of autonomous underwater vehicles. With a loose metric with IoU of 30%, the mean precision reaches 50.79%, and the mean recall reaches 55.96%.

Abstract [sv]

Detta examensarbete introducerar en innovativ metod för att upptäcka och göra segmentering av kablar för visuell inspektion. Kablar saknar specifika kännetecken och former, vilket gör att de är svåra att upptäcka på en rörig bakgrund. Den här metoden är baserad på kabelfärg och kabelbredd i ett specifikt scenario. Det kräver en split-and-merge-strategi för att detektera och segmentera. Denna metod kan användas för att inspektera kabelstatus på radiotorn för underhåll och bedömning av skador genom att analysera fotografier tagna av en obemannad luftfarkost (UAV). Denna metod för att detektera kablar kan också vara fördelaktig inom navigering av UAV och navigering av autonoma undervattensfordon. Med en lös metrik med IoU på 30% når medelprecisionen 50,79% och den genomsnittliga sensitiviteten når 55,96%.

Place, publisher, year, edition, pages
2019. , p. 42
Series
TRITA-EECS-EX ; 2019:828
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-271203OAI: oai:DiVA.org:kth-271203DiVA, id: diva2:1415992
Educational program
Master of Science - Embedded Systems
Supervisors
Examiners
Available from: 2020-03-20 Created: 2020-03-20 Last updated: 2020-03-20Bibliographically approved

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3637383940414239 of 204
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