Summary of Melon Fruit Detection and Quality Assessment Using Generative Ai-based Image Data Augmentation, by Seungri Yoon et al.
Melon Fruit Detection and Quality Assessment Using Generative AI-Based Image Data Augmentation
by Seungri Yoon, Yunseong Cho, Tae In Ahn
First submitted to arxiv on: 15 Jul 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 This paper investigates the use of generative AI models to create high-quality image datasets for training deep learning models like YOLO for real-time fruit detection. The authors employed MidJourney and Firefly tools to generate images of melon greenhouses and post-harvest fruits using text-to-image, pre-harvest image-to-image, and post-harvest image-to-image methods. They evaluated the AI-generated images using PSNR and SSIM metrics and tested the detection performance of the YOLOv9 model. The results showed that generative AI could produce realistic images, especially for post-harvest fruits, with the YOLOv9 model detecting generated images well. This study highlights the potential of AI-generated images for data augmentation in melon fruit detection and quality assessment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses artificial intelligence to create pictures of fruits and greenhouses. They want to train computers to recognize fruits quickly and accurately. To do this, they need lots of good-quality pictures. But these pictures are hard to make, especially since many aren’t available online. So, the authors used special tools called MidJourney and Firefly to create fake images that look like real ones. They then tested how well a computer model could recognize these fake images. The results showed that AI-generated images can be very realistic, which is great for training computers to detect fruits! |
Keywords
» Artificial intelligence » Data augmentation » Deep learning » Yolo
