ArticleSensors (Basel, Switzerland)2023
Insulator Defect Detection Based on ML-YOLOv5 Algorithm.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- SML-YOLO: a lightweight insulator defect detection algorithm for transmission lines under harsh weather conditions.Scientific reports · 2026Article
- Brain tumor detection on magnetic resonance imaging scans using the artificial intelligence-based You Only Look Once algorithm.The Journal of international medical research · 2026Article
- Insulator detection in transmission line based on Log AdaBoost.Scientific reports · 2025Article
- YOLOv5s-TC: An Improved Intelligent Model for Insulator Fault Detection Based on YOLOv5s.Sensors (Basel, Switzerland) · 2025Article
- Research on Lightweight Method of Insulator Target Detection Based on Improved SSD.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
To address the challenges of balancing accuracy and speed, as well as the parameters and FLOPs in current insulator defect detection, we propose an enhanced insulator defect detection algorithm, ML-YOLOv5, based on the YOLOv5 network. The backbone module incorporates depthwise separable convolution, and the feature fusion C3 module is replaced with the improved C2f_DG module. Furthermore, we enhance the feature pyramid network (MFPN) and employ knowledge distillation using YOLOv5m as the teacher model. Experimental results demonstrate that this approach achieved a 46.9% reduction in parameter count and a 43.0% reduction in FLOPs, while maintaining an FPS of 63.6. It exhibited good accuracy and detection speed on both the CPLID and IDID datasets, making it suitable for real-time inspection of high-altitude insulator defects.
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Registered trials
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