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Semantic segmentation is a hot area in computer vision (CV) research. The integration of computer vision and the film and television industry has put forward higher requirements for media data. At present, the analysis of film clips integrating high dynamics and continuous frames is one of the hot spots of video semantic segmentation. High-precision, high-volume data is the basis for proposing and improving deep learning models. However, the content labels of existing datasets are disorderly, which is difficult to uniformly evaluate and review, and the label category subjects are blurred, which directly affects the effectiveness of the model. Chinese martial arts films are a genre of films with Chinese national characteristics and the epitome of traditional Chinese culture. The "theme props" in martial arts films not only show various weapons in ancient China, but also integrate the values of "chivalry" and "righteousness" in traditional Chinese culture, becoming an indispensable part of martial arts movies. This study creatively performs artificial semantic segmentation of props in Chinese martial arts films, and establishes prop segmentation rules and general review standards. It includes frame pictures of movie clips at 4 frames per second, improved semi-automatic annotation of 6 props (weapons), and a visual version of pixel (mask) markers. As far as we know, this dataset is the largest semantic segmentation dataset on Chinese movie props to date. This dataset can be used for traditional inspection tasks in computer vision as well as related research on interdisciplinary and interdisciplinary techniques.

语义分割是计算机视觉（CV）研究领域的热门方向。计算机视觉与影视行业的融合，对媒体数据提出了更高的要求。当前，融合高动态与连续帧的影视片段分析，是视频语义分割的研究热点之一。高精度、大规模的数据是提出并优化深度学习模型的必要基础。然而，现有数据集的内容标签杂乱无章，难以进行统一的评估与审核，且标签类别主题模糊，直接制约模型的性能表现。中国武侠电影是兼具中国民族特色的电影类型，同时也是中华传统文化的缩影。武侠电影中的‘主题道具’，不仅展现了中国古代的各类兵器，更融合了中华传统文化中的‘侠义’与‘道义’价值观念，成为武侠电影不可或缺的组成部分。本研究开创性地针对中国武侠电影中的道具开展人工语义分割工作，并建立了道具分割规则与通用审核标准。该数据集包含每秒4帧的电影片段画面、针对6类道具（兵器）的优化后半自动标注，以及像素级（mask）标记可视化版本。据我们所知，本数据集是目前为止规模最大的中文电影道具语义分割数据集。本数据集可应用于计算机视觉领域的传统检测任务，以及跨学科相关技术研究。