[연구] 석사과정 이지은, SCIE 논문지(MDPI Electronics/Q2) 게재
- 스마트팩토리융합학과
- 조회수3553
- 2023-08-19
석사과정 이지은 학생(지도교수 : 정종필)의 연구(Real-Time Pose Estimation Based on ResNet-50 for Rapid Safety Prevention and Accident Detection for Field Workers)가 MDPI Electronics(Impact Factor: 2.9 (2022); 5-Year Impact Factor: 2.9 (2022))에 게재됐다.
https://www.mdpi.com/2079-9292/12/16/3513 / https://doi.org/10.3390/electronics12163513
논문요약 - The present study proposes a Real-Time Pose Estimation technique using OpenPose based on ResNet-50 that enables rapid safety prevention and accident detection among field workers. Field workers perform tasks in high-risk environments, and accurate Pose Estimation is a crucial aspect of ensuring worker safety. However, it is difficult for Real-Time Pose Estimation to be conducted in such a way as to simultaneously meet Real-Time processing requirements and accuracy in complex environments. To address these issues, the current study uses the OpenPose algorithm based on ResNet-50, which is a neural network architecture that performs well in both image classification and feature extraction tasks, thus providing high accuracy and efficiency. OpenPose is an algorithm specialized for multi-human Pose Estimation that can be used to estimate the body structure and joint positions of a large number of individuals in real time. Here, we train ResNet-50-based OpenPose for Real-Time Pose Estimation and evaluate it on various datasets, including actions performed by real field workers. The experimental results show that the proposed algorithm achieves high accuracy in the Real-Time Pose Estimation of field workers. It also provides stable results while maintaining a fast image processing speed, thus confirming its applicability in real field environments.