Summary of Exploring Machine Learning Engineering For Object Detection and Tracking by Unmanned Aerial Vehicle (uav), By Aneesha Guna et al.
Exploring Machine Learning Engineering for Object Detection and Tracking by Unmanned Aerial Vehicle (UAV)
by Aneesha Guna, Parth Ganeriwala, Siddhartha Bhattacharyya
First submitted to arxiv on: 19 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 presents a machine learning pipeline for developing a perception system for autonomous operations, focusing on object detection and tracking. The authors create a new dataset by collecting videos of moving objects, such as Roomba vacuum cleaners, simulating search and rescue scenarios in indoor environments. They refine the dataset through training on YOLOv4 and Mask R-CNN models, which are then deployed on a Parrot Mambo drone for real-time object detection and tracking. The experimental results demonstrate the effectiveness of the models, achieving an average loss of 0.1942 and 96% accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new dataset for autonomous operations by collecting videos of moving objects like Roomba vacuum cleaners. They make this data better by training it on special machine learning models called YOLOv4 and Mask R-CNN. Then, they use these trained models to help a drone find and track the objects in real-time. The results show that this system works well, with an average loss of 0.1942 and 96% accuracy. |
Keywords
» Artificial intelligence » Cnn » Machine learning » Mask » Object detection » Tracking
