Improved Method for Estimating Quality of Life Values of Images in Driving Scenes.

To reach the goals of low carbon emissions and a high quality of life (QOL) in Thailand, the Japan Science and Technology Agency (JST) and the Japan International Cooperation Agency (JICA) have undertaken the technical cooperation project known as the Smart Transportation Strategy for Thailand 4.0. As part of this project, an approach for estimating a QOL value from an image in a driving scene is proposed in this paper. The proposed method is based on a deep neural network. It assumes, as in the previous approach, that the QOL of a driving scene depends on the type and number of objects in the scene. Therefore, the proposed approach first applies a semantic segmentation method to an input image, and the QOL value is estimated by the deep neural network based on the MetaFormer. The loss function and optimizer are changed from the previous method to increase the accuracy of the proposed method. Smaller images than those used in the previous approach are used for faster processing. These approaches made the proposed method process more accurate and faster than the previous approach. The effectiveness of the proposed method was demonstrated by some experiments. [ABSTRACT FROM AUTHOR]