Summary of Cableinspect-ad: An Expert-annotated Anomaly Detection Dataset, by Akshatha Arodi et al.
CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset
by Akshatha Arodi, Margaux Luck, Jean-Luc Bedwani, Aldo Zaimi, Ge Li, Nicolas Pouliot, Julien Beaudry, Gaétan Marceau Caron
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 A machine learning-based approach is introduced to address the lack of transferability studies in real-world applications, specifically focusing on visual anomaly detection (VAD) for robotic power line inspection. The paper proposes a new dataset, CableInspect-AD, created and annotated by domain experts from Hydro-Québec, which includes high-resolution images with challenging real-world anomalies covering defects with varying severity levels. To enhance the PatchCore algorithm’s performance in scenarios with limited labeled data, an enhancement is proposed. A comprehensive evaluation protocol based on cross-validation is also presented to assess models’ performances. The study evaluates Enhanced-PatchCore for few-shot and many-shot detection, as well as Vision-Language Models for zero-shot detection, highlighting the value of CableInspect-AD as a challenging benchmark for the broader research community. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a special dataset called CableInspect-AD to help machines learn to find problems in real-world images. They took pictures of power lines with different kinds of defects and labeled them as normal or abnormal. The goal is to train machines to detect these defects, which can be challenging because the defects are different from what they’ve seen before. To make it easier for machines to learn, the researchers improved a popular algorithm called PatchCore. They also created a way to test how well the machines do by using pictures that haven’t been labeled yet. The results show that even with these improvements, machines still struggle to find all the defects, which is why this dataset is important and can help other researchers make better machines. |
Keywords
» Artificial intelligence » Anomaly detection » Few shot » Machine learning » Transferability » Zero shot
