Visual Relationship Detection for Workplace Safety Applications

Applications of object and visual relationship detection for safety and security applications are in its infancy. The state-of-the-art computer vision research is largely focused on improving mean average precision and mean average recall performance on standard, general datasets, such as the verbs in common objects in context and the visual genome dataset and rarely mention the potential of such models in safety and security scenarios. We propose to train and develop an object and visual relationship detection neural network to be used as part of the backend model for a decision support system. We use a naive Bayesian network to determine scenarios where our proposed object and visual relationship detection network is error prone. We also release a graphical user interface, which demonstrates how our backend neural network and naive Bayesian network can be used for hazardous workplace safety and security applications.