For Automatic High Beam (AHB), the authors are working on a study to classify beam of car headlights as 'High' and 'Low' by realizing the presence of vehicle on road from nighttime in-vehicle camera image using deep learning. In general, weights of deep learning model are pre-trained on a large dataset which are then used as initial weights for other tasks such as image recognition. However, it has been reported that even if the pre-trained weights are used as the initial weights, it is not a factor of accuracy improvement when domain differs. It is important to pre-train a model on a dataset that is suitable for target domain. Therefore, we propose a method to generate a pre-training dataset that can be easily created by using visual explanation which represents where a deep learning model is looking at when performs a task. Then, we applied the proposed pre-training dataset on the headlight beam classification in the nighttime in-vehicle camera image and verified the effectiveness of our method.

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