Home / Research Library / YOLOv6: A Single-Stage Object Detection Framework...
🤖 Artificial Intelligence OpenAlex

YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

📅 September 7, 2022 👤 Chuyi Li, Lulu Li, Hongliang Jiang et al. 📖 arXiv (Cornell University) 📊 1,750 citations

🤖 Plain-English Summary

For years, the YOLO series has been the de facto industry-level standard for efficient object detection. We carefully conducted experiments to validate the effectiveness of each component.

🔑 Key Findings

  • The YOLO community has prospered overwhelmingly to enrich its use in a multitude of hardware platforms and abundant scenarios.
  • In this technical report, we strive to push its limits to the next level, stepping forward with an unwavering mindset for industry application.
  • Considering the diverse requirements for speed and accuracy in the real environment, we extensively examine the up-to-date object detection advancements either from industry or academia.

💡 Why This Matters

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

Read the full paper
Access the original peer-reviewed research via OpenAlex.

View on DOI ↗

📋 Article Details

Category 🤖 Artificial Intelligence
Published Sep 07, 2022
Journal arXiv (Cornell University)
Authors Chuyi Li, Lulu Li, Hongliang Jiang, Kaiheng Weng, Yifei Geng
DOI 10.48550/arxiv.2209.02976
Citations 1,750
Source OpenAlex

More 🤖 Artificial Intelligence Research