Summary of Image Anomaly Detection and Prediction Scheme Based on Ssa Optimized Resnet50-bigru Model, by Qianhui Wan and Zecheng Zhang and Liheng Jiang and Zhaoqi Wang and Yan Zhou
Image anomaly detection and prediction scheme based on SSA optimized ResNet50-BiGRU model
by Qianhui Wan, Zecheng Zhang, Liheng Jiang, Zhaoqi Wang, Yan Zhou
First submitted to arxiv on: 20 Jun 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 novel deep learning framework combining Residual Network (ResNet) and Bidirectional Gated Recurrent Unit (BiGRU) is proposed for image anomaly detection. This model can predict potential injury types and provide early warnings by analyzing changes in muscle and bone poses from video images. The proposed network outperforms existing methods in terms of accuracy, demonstrating strong adaptability on four datasets. This approach has significant implications for predictive analysis in images, contributing to the sustainable development of human health and performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to find unusual patterns in videos is being developed using artificial intelligence. By looking at how athletes move and stand, it’s possible to predict if they might get hurt and suggest ways to prevent that. Most current methods use convolutional networks but they don’t work well because they look at too much information. The new method combines ResNet and BiGRU to analyze changes in muscle and bone poses from video images. It’s shown to be very accurate on four different datasets, which could lead to better ways of predicting injuries and improving human health. |
Keywords
» Artificial intelligence » Anomaly detection » Deep learning » Residual network » Resnet
