Summary of Xai-guided Insulator Anomaly Detection For Imbalanced Datasets, by Maximilian Andreas Hoefler et al.
XAI-guided Insulator Anomaly Detection for Imbalanced Datasets
by Maximilian Andreas Hoefler, Karsten Mueller, Wojciech Samek
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed pipeline utilizes state-of-the-art object detection and classification techniques to identify and categorize individual insulator anomalies in powerline inspections. By fine-tuning the model, the approach addresses dataset challenges like imbalance and motion-blurred images, increasing accuracy for anomalous insulators. The method also employs explainable-AI tools for precise localization and explanation of defects, contributing to the field of anomaly detection and predictive maintenance. The proposed approach achieves a significant 13% improvement in defect detection accuracy on real-world data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to use drones to inspect powerlines and find problems with insulators. Insulators are important because if they malfunction, it can cause transmission disruptions. The new method uses computer vision techniques to detect and classify individual insulator defects. It also helps deal with issues like unbalanced data and blurry images by adjusting the model’s focus on finding anomalies. This approach could be useful for predictive maintenance and detecting problems before they become serious. |
Keywords
» Artificial intelligence » Anomaly detection » Classification » Fine tuning » Object detection
