Summary of Performance Evaluation Of Semi-supervised Learning Frameworks For Multi-class Weed Detection, by Jiajia Li et al.
Performance Evaluation of Semi-supervised Learning Frameworks for Multi-Class Weed Detection
by Jiajia Li, Dong Chen, Xunyuan Yin, Zhaojian Li
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 presents a semi-supervised learning framework for multi-class weed detection using object detection frameworks FCOS and Faster-RCNN. The proposed approach utilizes a generalized student-teacher framework with an improved pseudo-label generation module to produce reliable pseudo-labels for unlabeled data, and an ensemble student network to enhance generalization. Experimental results show that the method achieves comparable performance to supervised learning approaches with only 10% of labeled data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers have developed a new way to identify different types of weeds in crops using machine learning and deep learning. They used a special type of learning called semi-supervised learning, which is more efficient than traditional methods that require lots of labeled data. The method works by training two models (FCOS and Faster-RCNN) to detect weeds, and then using one model to help the other learn from unlabeled images. This approach was tested on two datasets and showed similar accuracy to traditional methods but with much less labeled data. |
Keywords
* Artificial intelligence * Deep learning * Faster rcnn * Generalization * Machine learning * Object detection * Semi supervised * Supervised
