Insu-YOLO: An Insulator Defect Detection Algorithm Based on Multiscale Feature Fusion
Open accessAbstract
To keep the balance of precision and speed of unmanned aerial vehicles (UAVs) in detecting insulator defects during power inspection, an improved insulator defect identification algorithm, Insu-YOLO, which is based on the latest YOLOv8 network, is proposed in this paper. Firstly, to lower the computational complexity of the network, the GSConv module is introduced in the backbone and neck network. In the neck network, a lightweight content-aware reassembly of features (CARAFE) structure is adopted to better utilize the feature information for upsampling, which enhances the feature fusion capability of Insu-YOLO. Additionally, Insu-YOLO enhances the fusion between shallow and deep feature maps by adding an extra object detection layer, thereby increasing the accuracy for detecting small targets. The experimental results indicate that the mean average precision of Insu-YOLO reaches 95.9%, which is 3.95% higher than the YOLOv8n baseline model, with a memory usage of 9.2 MB. Moreover, the detection speed of Insu-YOLO is 87 frames/s which achieves the purpose of real-time identification of insulator defects.
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YOLOD, using Gaussian priors in front of YOLOX detection heads, improved bounding box regression and achieved an AP of 73.9% on power line insulator images.
“YOLOD, a unique model addressing uncertainty in object detection, used Gaussian priors in front of YOLOX detection heads. Improved bounding box regression and achieved an AP of 73.9%.”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [131]
The Experimental Results (§4.4) and Conclusions (§5; page numbers unavailable) report results for Insu-YOLO, not the claimed YOLOD result. Searches for YOLOD, Gaussian priors, YOLOX detection heads, and 73.9% found no passage addressing that claim. (LLM, conf=0.76)
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Tian, M., Li, J., Cao, B., Dai, Y., Ye, H., & Long, H. (2025) · International Conference on Power System and Energy Internet (PoSEI) · doi:10.1109/posei67825.2025.11382249
A railway surface defect detector with frequency–state modeling and multi-scale reverse fusion
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- Authors
- Chen, Y., Liu, H., Chen, J., Hu, J., & Zheng, E.
- Affiliations
- China Jiliang University (CN)
- Corresponding author
- Zheng, Enhui
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2023-07-25
- Volume
- 12
- Issue
- 15
- Pages
- 3210
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W4385273301
- Cited by
- 78
- References
- 21
- Citations by year
- 2026: 17 · 2025: 33 · 2024: 22 · 2023: 6
- FWCI
- 7.69
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Power Line Inspection Robots
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- insulator defect detection, power line inspection, UAV inspection, YOLOv8, GSConv, CARAFE upsampling, multi-scale feature fusion, small object detection, lightweight object detection, real-time detection
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