ArticleSensors (Basel, Switzerland)2023
A Lightweight Algorithm for Insulator Target Detection and Defect Identification.
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 7 papers.
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Who cites it
7 citing papers in PubMed.
- FS-YOLO: A Lightweight Insulator Defect Detection Method Based on Multi-Level Feature Fusion and Localization Quality Estimation.Sensors (Basel, Switzerland) · 2026Article
- A Lightweight Insulator Defect Detection Model for Edge Computing Devices: PEBL-YOLO.Sensors (Basel, Switzerland) · 2026Article
- Insulator Defect Detection Algorithm Based on Improved YOLOv11n.Sensors (Basel, Switzerland) · 2025Article
- Dental bur detection system based on asymmetric double convolution and adaptive feature fusion.Scientific reports · 2024Article
- An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images.Parasites & vectors · 2024Article
- Efficient Cross-Modality Insulator Augmentation for Multi-Domain Insulator Defect Detection in UAV Images.Sensors (Basel, Switzerland) · 2024Article
- UAV Cluster Mission Planning Strategy for Area Coverage Tasks.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
9 authors.
Funding
Abstract
The accuracy of insulators and their defect identification by UAVs (unmanned aerial vehicles) in transmission-line inspection needs to be further improved, and the model size of the detection algorithm is significantly reduced to make it more suitable for edge-end deployment. In this paper, the algorithm uses a lightweight GhostNet module to reconstruct the backbone feature extraction network of the YOLOv4 model and employs depthwise separable convolution in the feature fusion layer. The model is lighter on the premise of ensuring the effect of image information extraction. Meanwhile, the ECA-Net channel attention mechanism is embedded into the feature extraction layer and PANet (Path Aggregation Network) to improve the recognition accuracy of the model for small targets. The experimental results show that the size of the improved model is reduced from 244 MB to 42 MB, which is only 17.3% of the original model. At the same time, the mAp of the improved model is 0.77% higher than that of the original model, reaching 95.4%. Moreover, the mAP compared with YOLOv5-s and YOLOX-s, respectively, is improved by 1.98% and 1.29%. Finally, the improved model is deployed into Jetson Xavier NX and run at a speed of 8.8 FPS, which is 4.3 FPS faster than the original model.
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Registered trials
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