用YOLOv8检测乐谱元素并联合训练装配，提升真实场景下的识别准确率。
Toward a More Complete OMR Solution
- 基于YOLOv8的乐谱元素检测器，提升视觉识别精度
- 联合训练检测与装配流程，在MUSCIMA++上超越理想检测模型
- 适合需要端到端乐谱数字化的科研与开发人员
光学乐谱识别（OMR）旨在将乐谱图像转换为数字格式。传统方法采用多阶段流水线：先检测图像中的音乐符号（对象检测），再组装成完整乐谱（符号装配）。以往研究常假设检测结果完美，忽略了实际误差影响。本研究以MUSCIMA++ v2.0数据集为基础，将乐谱表示为带成对关系的图结构，同时优化检测与装配两阶段。首先提出基于YOLOv8的音乐符号检测器，提升检测性能；其次设计监督训练的装配流程，直接利用检测输出完成符号组装。实验表明，该模型在真实检测误差下仍优于仅在理想检测条件下训练的现有模型。结合新提出的评估指标，该工作推动了更完整的OMR解决方案的发展。
原文摘要 · Abstract (English)
Optical music recognition (OMR) aims to convert music notation into digital formats. One approach to tackle OMR is through a multi-stage pipeline, where the system first detects visual music notation elements in the image (object detection) and then assembles them into a music notation (notation assembly). Most previous work on notation assembly unrealistically assumes perfect object detection. In this study, we focus on the MUSCIMA++ v2.0 dataset, which represents musical notation as a graph with pairwise relationships among detected music objects, and we consider both stages together. First, we introduce a music object detector based on YOLOv8, which improves detection performance. Second, we introduce a supervised training pipeline that completes the notation assembly stage based on detection output. We find that this model is able to outperform existing models trained on perfect detection output, showing the benefit of considering the detection and assembly stages in a more holistic way. These findings, together with our novel evaluation metric, are important steps toward a more complete OMR solution.
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