Summary of Accelerating Object Detection with Yolov4 For Real-time Applications, by K. Senthil Kumar et al.
Accelerating Object Detection with YOLOv4 for Real-Time Applications
by K. Senthil Kumar, K.M.B. Abdullah Safwan
First submitted to arxiv on: 17 Oct 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 paper proposes an improved object detection architecture that builds upon Convolutional Neural Network (CNN) and You Only Look Once (YOLOv4) models. The authors aim to address the limitations of traditional object detection methods in real-time applications, where objects are dynamic and complex algorithms are used. By introducing modifications to the YOLOv4 model, they achieve higher accuracy in detecting small objects in images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to detect objects in pictures and videos. It’s an important tool for things like security cameras and self-driving cars. The problem with current object detection methods is that they can’t handle moving objects or complex environments. To solve this, the authors use a powerful machine learning model called Convolutional Neural Network (CNN) and modify it to make it better. Their new method is more accurate at detecting small objects. |
Keywords
» Artificial intelligence » Cnn » Machine learning » Neural network » Object detection
