Summary of From Classical Techniques to Convolution-based Models: a Review Of Object Detection Algorithms, by Fnu Neha et al.
From classical techniques to convolution-based models: A review of object detection algorithms
by Fnu Neha, Deepshikha Bhati, Deepak Kumar Shukla, Md Amiruzzaman
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper reviews object detection frameworks, categorizing them into classical computer vision techniques and Convolutional Neural Networks (CNN)-based detectors. It compares major CNN models, discussing their strengths and limitations. The review highlights the significant advancements in object detection through deep learning and identifies key areas for further research to improve performance. The paper covers traditional methods, which relied on handcrafted features and shallow models, struggling with complex visual data and showing limited performance. Deep learning addresses these limitations by automatically learning rich, hierarchical features directly from data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers can detect objects in pictures. Right now, computers are really good at finding simple things like cats or cars. But they struggle to find more complicated things like people or animals. The problem is that most computer programs rely on humans telling them what to look for and they’re not very good at figuring out what’s important. A new way of doing this called deep learning has made a big difference. It lets computers learn from lots of pictures and get better and better at finding things. This paper looks at how different ways of using deep learning have worked and where there’s still room for improvement. |
Keywords
» Artificial intelligence » Cnn » Deep learning » Object detection
