# A Baseline for General Music Object Detection with Deep Learning
## 2 Background on Music Object Detection  
Traditionally, OMR has been approached by workflows composed of several stages, as outlined in the previous section. In addition, these stages were further subdivided into smaller steps. Inside of the music object detection stage, the key step used to be the staff-line detection and removal [20]. Although staves are essential for the understanding of music notation, their presence hindered the isolation of musical primitives using classical algorithms such as connected-components analysis. That is why, for many years, much research was devoted to improving staff-line removal [21]. Currently, thanks to the use of deep neural networks, the staff-line removal can be considered a solved problem, with selectional auto-encoders outperforming all previously existing methods given a sufficient amount of training data [22]. However, even with an ideal staff-line removal algorithm, isolating musical symbols by means of connected components remains problematic, since multiple primitives could be connected to each other (e.g., a beam group can be a single connected component that includes several heads, stems, and beams) or a single unit can have multiple disconnected parts (e.g., a fermata, voltas, f-clef). The second case is particularly severe in the context of handwritten notation, where symbols can be written with such a high variability (e.g., detached noteheads) that modeling all possible appearances becomes intractable. Recently, it has been shown that the use of region-based machine learning models is an alternative that can deal with the stage of music object detection holistically. These models have been widely developed in the computer vision community, attaining high performance in detecting objects in images by using convolutional neural networks. In addition to the performance, a compelling advantage is that these models can be trained in an end-to-end manner, that is, by merely providing pairs of images and positions where the objects to be detected are located; these models, therefore, make it possible to bypass several stages of the classical OMR workflow by directly detecting symbols in music score images. [PERSON] et al. [23] presented the first work that considered region-based convolutional neural networks for the task of music object detection. They proposed a sliding-window based approach, that cuts the image in a context-sensitive way into smaller chunks that contain no more than one staff and ran a Faster R-CNN detector to obtain the positions and classes of all symbols in the cropped image. While the evaluation is limited to the detection performance on small image chunks instead of the entire images, the extension of this approach to full pages of handwritten music scores, written in mensural notation, is reported to yield promising results [24]. [PERSON] jr. et al. [25] use a different approach: instead of applying an object detection model directly, they use a semantic segmentation model and a subsequent detection stage. More specifically, the semantic segmentation is done with the U-Net architecture [26]. The overall detection problem is broken down into a set of binary pixel classification problems and subsequently uses a connected components detector to arrive at the final detection proposals. The object detection results are reported in terms of F-scores, broken down by symbol class with no aggregate result, and the experiments are done only for a subset of the symbol classes available in the MUSCIMA++ dataset; on the other hand, the notation reconstruction step is subsequently applied, and the object detection is evaluated in terms of the subsequent MIDI inference. The Deep Watershed Detector proposed by [PERSON] et al. [27] is another attempt to solve music object detection by training a convolutional neural network to learn a custom energy function that is used in a watershed transformation to perform semantic segmentation of an entire score. They evaluate their approach on the DeepScores and the MUSCIMA++ dataset.