基于YOLO目标检测和深度学习的工业领域安全系统

JeongYoon Rhee, Junhyuk Park, JaeIn Lee, HyunTae Ahn, L. Pham, Jaewook Jeon
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引用次数: 0

摘要

本文提出了一种可用于各种工业现场的安全系统。安全系统通过线检测方法检测边界,并通过摄像头拍摄的图像识别使用YOLO (You Only Look Once)的人。该系统利用深度图像判断被检测人群中哪些人处于危险范围内。因此,本文在提出的硬件中,包括选择特定的YOLO模型,通过深度学习训练YOLO模型来提高性能,深度数据校正,线检测方法以及系统优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Safety System for Industrial Fields using YOLO Object Detection with Deep Learning
This paper proposes a safety system that can be used in various industrial field situations. The safety system detects boundaries with a line detection method and identifies people using YOLO (You Only Look Once) from images captured through a camera. And using the depth image, this system determines which individuals are within the danger range among the detected people. Therefore, this paper includes the selection of a specific YOLO model, performance improvement through training YOLO models with deep learning, depth data correction, line detection method, and system optimization in the proposed hardware.
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