Cost-Efficient Driver Distraction Detection System: Transformer-Based Classification on Bayer Image

Driver State Monitoring (DSM) is paramount for improving driving safety for both drivers of ego-vehicles and their surrounding road users, increasing public trust, and supporting the transition to autonomous driving. This paper introduces a Transformer-based classifier for DSM using an in-vehicle camera capturing raw Bayer images. Compared to traditional RGB images, we opt for the original Bayer data, further employing a Transformer-based classification algorithm. Experimental results prove that the accuracy of the Bayer Color-filled type images is only 0.61% lower than that of RGB images. Additionally, the performance of Bayer data is closely comparable to RGB images for DSM purposes. However, utilizing Bayer data can offer potential advantages, including reduced camera costs, lower energy consumption, and shortened Image Signal Processing (ISP) time. These benefits will enhance the efficacy of DSM systems and promote their widespread adoption.