Abstract:

The non-compliant interaction between workers， construction machinery and construction appliances in the construction site is an important cause of safety accidents. Therefore， a visual relationship detection method based on construction scene is proposed. Firstly， convolution neural network is used to build entity detection and relationship detection branches to extract entity features and relationship features in construction scene. Secondly， visual module， semantic module and space module are constructed to learn the extracted features， so that the network can fully perceive and understand visual information， semantic information and spatial information. Finally， a graphical contrastive loss function is designed to improve the visual relationship detection performance. The experimental results on the self-made construction relationship detection data set show that the proposed method achieves the R@20， R@50， R@100 recall rate of 75.89%， 77.64% and 78.93%. The proposed method has good visual relationship detection performance， and can accurately detect the objects and their interactions in the construction scene.

Key words: computer application technology, visual relationship detection, construction scene, convolutional neural network, scene graph, image understanding