Summary of Class Imbalance in Object Detection: An Experimental Diagnosis and Study Of Mitigation Strategies, by Nieves Crasto
Class Imbalance in Object Detection: An Experimental Diagnosis and Study of Mitigation Strategies
by Nieves Crasto
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 A novel benchmarking framework is introduced to address the issue of foreground-foreground class imbalance in object detection, a crucial task in computer vision. The proposed framework utilizes the YOLOv5 single-stage detector and creates a 10-class long-tailed dataset, COCO-ZIPF, based on the COCO dataset. This dataset reflects common real-world detection scenarios with limited object classes. The study compares three established techniques: sampling, loss weighing, and data augmentation to improve YOLOv5’s performance on COCO-ZIPF. The results show that data augmentation methods, specifically mosaic and mixup, significantly enhance the model’s mean Average Precision (mAP) by introducing more variability and complexity into the training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Object detection is a crucial task in computer vision, but it can be hindered by dataset imbalances. This study introduces a new benchmarking framework to address this issue. The framework uses YOLOv5, a popular single-stage detector, and creates a new dataset that reflects real-world scenarios. Three techniques are compared to see which works best: sampling, loss weighing, and data augmentation. The results show that one type of data augmentation is the most effective in improving the model’s performance. |
Keywords
* Artificial intelligence * Data augmentation * Mean average precision * Object detection
