Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images

In this paper, the authors developed a safety guardrail detection model based on convolutional neural network (CNN). An augmented data set is generated with the addition of background image to guardrail 3D models and used as training set. Transfer learning is utilized and the Visual Geometry Group architecture with 16 layers (VGG-16) model is adopted to construct the basic features extraction for the neural network. In the CNN implementation, 4000 augmented images were used to train the proposed model, while another 2000 images collected from real construction jobsites and 2000 images from Google were used to validate the proposed model. The proposed CNN-based guardrail detection model obtained a high accuracy of 96.5%. In addition, this study indicates that the synthetic images generated by augment technology can be used to create a large training dataset, and CNN-based image detection algorithm is a promising approach in construction jobsite safety monitoring.