Deep learning-based detection of power transmission lines using YOLOv4 and YOLOv8
Tugce Nur Karadeniz, Sami Ekici, Engin Avci · 2025
Read Deep learning-based detection of power transmission lines using YOLOv4 and YOLOv8 by Tugce Nur Karadeniz, Sami Ekici, Engin Avci (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12816585.txt.
Description
Inspecting power lines by eye is slow, costly, and sometimes dangerous, so drones with smart cameras are taking over the job. Tugce Nur Karadeniz, Sami Ekici, and Engin Avci compared two generations of the YOLO detection family for spotting transmission lines in 2025.
Their study reviews how the one-pass YOLO algorithm finds and classifies objects in a single network sweep, then tests versions head to head on line images. Every YOLOv8 variant beat YOLOv4 on the detection measures. Precision, recall, and F1 scores for the newer models all ran above 99 percent in their tests, with strong average precision as well. The authors present the newer family as the practical choice for drone-based grid inspection.
Catalog record
| CREATOR | Tugce Nur Karadeniz, Sami Ekici, Engin Avci |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 43 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |