The lighting image of a car lamp is one of the important factors influencing the exterior design of a car, but it is difficult to find an evaluation method for quantitatively evaluating the lighting image. If there is a method of quantitatively evalua...
The lighting image of a car lamp is one of the important factors influencing the exterior design of a car, but it is difficult to find an evaluation method for quantitatively evaluating the lighting image. If there is a method of quantitatively evaluating how uniform the lighting image of the lamp is, it will be easy to manage the quality of the lighting image. This study attempted to determine whether it is possible to quantitatively evaluate the lighting image data of a car lamp using a deep learning algorithm that learned a good lighting image and a poor lighting image of a car lamp using deep learning of image data. CNN algorithms were used for deep learning of image data, and learning was conducted using images of various automobile position and tail lamp lighting. The learned algorithm classified good lighting images and poor lighting image data with a probability of more than 90% for the new lighting image data, and was able to provide lighting uniformity scores for the image data. The method of learning deep learning algorithms could help develop evaluation methods that are difficult to develop in conventional ways or quantitative evaluation methods for complex data.