Summary of Dart: An Automated End-to-end Object Detection Pipeline with Data Diversification, Open-vocabulary Bounding Box Annotation, Pseudo-label Review, and Model Training, by Chen Xin et al.
DART: An Automated End-to-End Object Detection Pipeline with Data Diversification, Open-Vocabulary Bounding Box Annotation, Pseudo-Label Review, and Model Training
by Chen Xin, Andreas Hartel, Enkelejda Kasneci
First submitted to arxiv on: 12 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 presents DART, an innovative pipeline for real-time object detection that automates data collection and annotation. The four-stage process eliminates manual labor, achieving high accuracy across diverse scenarios. It uses subject-driven image generation, open-vocabulary object detection, review by large multimodal models, and training of real-time object detectors (YOLOv8 and YOLOv10). DART is applied to a self-collected dataset of construction machines, Liebherr Product, which contains over 15K high-quality images across 23 categories. The pipeline significantly increases average precision from 0.064 to 0.832, ensuring ease of exchangeability, extensibility, and adaptability to customized environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DART is a new way to detect objects in real-time. It makes it easier to find things by generating images, detecting objects, reviewing the results, and training models to get better. The current version of DART works really well, with an average precision score increasing from 0.064 to 0.832. This means it can accurately detect objects in different situations. |
Keywords
» Artificial intelligence » Image generation » Object detection » Precision
