For DL the colorspace is irrelevant as long as it's consistent between training and inference. I.e. if you train your model in RGB, it better be receiving RGB and not something else from the video feed or input images. Interestingly, models can also be trained in YUV (including its downsampled variants like YUYV), which can make them a tiny bit more efficient, both because there's less data, and because most camera chips can output YUV raw data directly. The net will learn whatever transform it needs to learn to make sense of the data.