Spatial Information and Deep Neural Networks-Based Smart Design for Light Environment of Museum Exhibition

It is a practical issue to make professional resource planning towards low light environment such as museums, so as to achieve the optimal visual experience. This work aims at such scenes, and manages to deal with them from the perspective of vision computing-based optimal planning. Therefore, a smart optimal resource planning method for low light environment in museum based on convolutional vision computing network, is proposed in this paper. Based on the expected visual effect and light environment adaptation data in realistic situation, a convolution operations-based neural computing structure is developed to implement extraction and fusion of visual characteristics. And output of this part is further utilized for following digital evaluation of visual effect. At last, simulation experiments of the proposal are conducted to assess running effect of the proposal. The results show that time efficiency of proposal is reduced by 23.8% and the accuracy rate is improved by 12% compared with the U-net model. It is concluded that the proposal can be utilized in realist engineering applications to implement such tasks.