Pre-training dataset generation for classifying beams of vehicle headlights from nighttime camera image

Toward realizing Automatic High Beam, we are working on a study to classify the beam mode of headlights as "High" and "Low" from night-time in-vehicle camera images using deep learning. Deep learning requires a large amount of training data, and the more effective data are used for training, the better the accuracy of the model is. However, since creating training data is very time consuming, it is often not possible to prepare sufficient data. One of the approaches to perform any task with limited data is to use the learned weights of another task as initial weights of the model and finetune the model with actual limited data. In the image classification, the weights learned on large-scale dataset such as ImageNet are used as initial weights to perform another task. However, in such initialization, if the dataset domain differs from the target domain, there is a report that it is not a factor of the accuracy improvement and it is necessary to learn a model on the pre-training dataset which is suitable to the target domain. Therefore, in this study, we propose a method to create a pre-training dataset easily suitable for the target domain by utilizing public dataset. Experiment results show that proposed method improves the classification accuracy of "High" and "Low" of headlights beam from nighttime in-vehicle camera image.