@inproceedings{a77eae620af94bcfb81997c02b966fff,
title = "Detection of Power Line Insulator Defects Using YOLOv10-N",
abstract = "Visual inspection remains a common approach for assessing composite insulators, with unmanned aerial vehicles (UAVs) increasingly preferred due to their efficiency and reduced error rates. Recent developments have integrated artificial intelligence (AI) algorithms directly into UAV hardware to enable faster processing; however, such systems require optimized models owing to limited onboard computing resources. The recently introduced YOLOv10-N model, which offers greater efficiency compared to its predecessors, demonstrates potential for detecting insulator defects on resource-constrained UAV platforms. This study evaluates the effectiveness of YOLOv10-N for this application.",
keywords = "Embedded systems, Image, Insulator, Visual inspection, YOLO",
author = "Papi, \{Kgampu Shawn\} and \{van Zyl\}, \{Terence L.\}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 6th Southern African Conference for Artificial Intelligence Research, SACAIR 2025 ; Conference date: 01-12-2025 Through 05-12-2025",
year = "2026",
doi = "10.1007/978-3-032-11733-5\_33",
language = "English",
isbn = "9783032117328",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "531--540",
editor = "Aurona Gerber and Pillay, \{Anban W.\}",
booktitle = "Artificial Intelligence Research - 6th Southern African Conference, SACAIR 2025, Proceedings",
address = "Germany",
}