Detecting staffs, systems, and measures, collectively known as layout analysis, matters for Optical Music Recognition (OMR), both because most systems today expect staff-level inputs, and because even if these are replaced by systems that can process the whole page, the staffs and systems are useful elements of OMR user interfaces and applications. It receives comparatively little attention, which is justified, as it avoids many class im- balance, small object, and object assembly phenomena, which is what makes OMR difficult and interesting. However, the main publicly available tool for layout analysis, the MeasureDetector, has not been updated for several years, and off-the-shelf object detection has progressed: not just in accuracy, but also in speed. Therefore, in this paper, we bring an update on the performance of OMR layout analysis with the state-of-the-art YOLO platform. Compared to the MeasureDetector, it achieves a similar or better accuracy across both in-domain and out-of-domain tests over three different datasets that we harmonized, it is more than 20x faster, and requires more than 4 times less memory