Deepscores 数据集

Description

DeepScores 数据集的目标是推进小物体识别的最新技术，并将物体识别问题置于场景理解的背景下。 DeepScores 包含高质量的乐谱图像，分为 300 0 000 张书面音乐，其中包含不同形状和大小的符号。拥有近一亿个小对象，这使得我们的数据集不仅独一无二，而且是最大的公共数据集。 DeepScores 带有用于对象分类、检测和语义分割的基本事实。因此，DeepScores 总体上对计算机视觉提出了相关挑战，超出了光学音乐识别 (OMR) 研究的范围。

The accompaning paperThe DeepScoresV2 Dataset and Benchmark for Music Object Detectionpublished at ICPR2020 can be found here:

https://digitalcollection.zhaw.ch/handle/11475/20647

A toolkit for convenient loading and inspection of the data can be found here:

https://github.com/yvan674/obb_anns

Code to train baseline models can be found here:

https://github.com/tuggeluk/mmdetection/tree/DSV2_Baseline_FasterRCNN

https://github.com/tuggeluk/DeepWatershedDetection/tree/dwd_old

Abstract
We present the DeepScores dataset with the goal of ad-
vancing the state-of-the-art in small objects recognition,
and by placing the question of object recognition in the
context of scene understanding. DeepScores contains high

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DeepScores contains high quality images of musical scores, partitioned into 300,000 sheets of written music that contain symbols of different shapes and sizes. For advancing the state-of-the-art in small objects recognition, and by placing the question of object recognition in the context of scene understanding.

什么是微小物体当目标距离成像系统较远时，目标的像只占场景图像中单个或几个像素，这类微小尺寸目标的形态特征就近似与一个点目标。微小物体检测的难点1、小目标的尺度过小，基于手工特征提取的算法大多利用目标周围的邻域信息表达目标2、小目标本身的特征不够明显，容易受图像噪声的干扰而最终导致无检测和漏检测。现有方法1、使用高分辨率摄像头较少成像过程中的干扰和噪声2、提取识别算法方面，有两大假设：...

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