Browse State-of-the-Art › Insulator Defect Detection
Insulator Defect Detection
3 papers with code · 0 benchmarks · 0 datasets archive 2025-07-28
In the Insulator Defect Detection task, the primary objective is to automatically detect defects in electrical insulators using computer vision and machine learning techniques. Insulators are critical components in power transmission systems, responsible for supporting and isolating high-voltage power lines to prevent leakage and short circuits. Over time, insulators may develop defects such as cracks, contamination, and breakage due to exposure to harsh environmental conditions like wind, rain, dirt, and high temperatures. If these defects are not detected and addressed promptly, they can lead to power system failures or even severe accidents. Therefore, automating the detection of insulator defects enhances the safety and reliability of power systems while reducing the workload and cost associated with manual inspections. This task typically involves analyzing image or video data to accurately identify and locate various types of defects on insulators.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
No benchmark for this task in the archive.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
No dataset record in the archive lists this task.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
3 shown of 3 papers with code (5 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
-
26 Aug 2024 1 repository listedInitially, the backbone network of IDD-YOLO employs GhostNet for feature extraction.
-
19 Aug 2024 1 repository listedIn order to achieve real-time detection of insulator defects in transmission lines, this paper proposes an improved insulator defect detection algorithm based on the YOLOv5s model, named IDD-YOLOv5.
-
24 Jun 2024 1 repository listedInsulator defect detection is of great significance to ensure the normal operation of power transmission and distribution networks.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections