高分辨率视频数据集助力钢铁废料中铜杂质的自动识别
SteelDS: A High-Resolution Video Dataset of E40 Steel Scrap for Object Detection and Instance Segmentation
- 采集实验室环境下传送带上的碎钢铜废料视频，标注像素级分割掩码
- 含24,297帧、396个钢物、101个铜物，覆盖不同密度与间距场景
- 适合做工业废料自动分拣的检测与分割模型研究
该数据集提供在受控实验环境下拍摄的高分辨率视频序列，记录了输送带上破碎的E40级钢与铜废料。数据反映工业磁选后的处理阶段，此阶段通常需人工清除铜杂质。数据集包含24,297帧标注图像，分为五个子集，涵盖396个钢类物体和101个铜类物体，并按尺寸分类。数据支持材料分类、目标检测与实例分割模型的开发。通过模拟真实工业分拣中的物体间距与密度变化，提升模型鲁棒性。标注包含像素级分割掩码与材质类别。该数据集可作为评估自动化分拣算法识别复杂异质钢废料中铜杂质的基准。
原文摘要 · Abstract (English)
This dataset provides high-resolution, annotated video sequences of shredded E40-grade steel and copper scrap on a conveyor belt. Captured in a controlled laboratory environment, the data reflects the industrial post-magnetic sorting stage, where manual intervention is typically required to remove copper contaminants. The dataset comprises 24,297 labeled frames across five subsets, featuring 396 steel and 101 copper objects categorized by size. It supports the development of machine learning models for material classification, object detection, and instance segmentation. Variations in object spacing and density are included to simulate realistic industrial sorting conditions. Ground truth annotations include pixel-wise segmentation masks and material classes. This dataset serves as a benchmark for evaluating automated sorting algorithms aiming to identify copper impurities within complex, heterogeneous steel scrap streams.
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