Publication:
Development of AI Vision Inspection System for UAV imagery Surveillance of Transmission Towers

Simple item page
dc.citedby0
dc.contributor.authorHussein S.Y.S.H.en_US
dc.contributor.authorDziyauddin R.A.en_US
dc.contributor.authorMuhtazaruddin M.N.en_US
dc.contributor.authorYaghoobi Y.J.en_US
dc.contributor.authorDin N.M.en_US
dc.contributor.authorid59536036900en_US
dc.contributor.authorid57198512001en_US
dc.contributor.authorid55578437800en_US
dc.contributor.authorid59536737200en_US
dc.contributor.authorid9335429400en_US
dc.date.accessioned2025-03-03T07:44:59Z
dc.date.available2025-03-03T07:44:59Z
dc.date.issued2024
dc.description.abstractHigh-voltage transmission line networks are essential for electricity delivery and require proactive maintenance to prevent breakdowns due to steady demand and full-capacity operation. Federal and state regulations require annual inspections of the right-of-way (ROW) and transmission infrastructure. Traditionally, helicopters are used for these inspections, but they are costly. Another issue is that power outages and financial losses are still caused by vegetation encroachment in power line corridors. To address this, we propose an automated method combining robotics, photogrammetry, and computer vision. Unmanned aerial vehicles (UAVs) present a viable monitoring alternative because of their speedy and cost-effective high-resolution image capturing. However, segmenting vegetation encroachment in these images is difficult due to the complexity and pixel imbalance. We propose a deep learning-based approach to address these challenges by dividing the dataset into 3 classes: power line, background, and vegetation. Using the UAV- VEPL-NET dataset and a Convolutional Neural Network (CNN) model, initial training will be conducted with the help of Roboflow software. This approach will help the system detect with high accuracy. The final model will then be embedded into the UAV for testing. Overall, the average precision of the demo training in Roboflow was 93%, whereas the accuracy for every class was 93% for the power line, 89% for the background, and 96% for the vegetation. ? 2024 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICSSA62312.2024.10788575
dc.identifier.scopus2-s2.0-85216512538
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85216512538&doi=10.1109%2fICSSA62312.2024.10788575&partnerID=40&md5=a3a6b400108eec8656189c809246c645
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36830
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2024 5th International Conference on Smart Sensors and Application: Shaping the Future of Intelligent Innovation, ICSSA 2024
dc.subjectConvolutional neural networks
dc.subjectDeep neural networks
dc.subjectFailure analysis
dc.subjectHVDC power transmission
dc.subjectInspection equipment
dc.subjectOutages
dc.subjectPower distribution lines
dc.subjectPower transmission lines
dc.subjectRisk management
dc.subjectUnmanned aerial vehicles (UAV)
dc.subjectAerial vehicle
dc.subjectConvolutional neural network
dc.subjectInspection system
dc.subjectPower lines
dc.subjectRight of way
dc.subjectRights-of-way
dc.subjectRoboflow
dc.subjectTransmission inspection system
dc.subjectUnmanned aerial vehicle
dc.subjectVision inspection systems
dc.titleDevelopment of AI Vision Inspection System for UAV imagery Surveillance of Transmission Towersen_US
dc.typeConference paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS