| Title |
Image-Based Automatic Detection of Construction Helmets Using R-FCN and Transfer Learning |
| Authors |
박상윤(Park, Sangyoon) ; 윤상현(Yoon, Sanghyun) ; 허준(Heo, Joon) |
| DOI |
https://doi.org/10.12652/Ksce.2019.39.3.0399 |
| Keywords |
건설안전;물체 탐지;딥러닝;인공신경망 Construction safety;Object detection;Deep learning;Neural network |
| Abstract |
In Korea, the construction industry has been known to have the highest risk of safety accidents compared to other industries. Therefore, in order to improve safety in the construction industry, several researches have been carried out from the past. This study aims at improving safety of labors in construction site by constructing an effective automatic safety helmet detection system using object detection algorithm based on image data of construction field. Deep learning was conducted using Region-based Fully Convolutional Network (R-FCN) which is one of the object detection algorithms based on Convolutional Neural Network (CNN) with Transfer Learning technique. Learning was conducted with 1089 images including human and safety helmet collected from ImageNet and the mean Average Precision (mAP) of the human and the safety helmet was measured as 0.86 and 0.83, respectively. |