首个工业传送带裂纹序列图像数据集，助力智能检测。
BeltCrack: the First Sequential-image Industrial Conveyor Belt Crack Detection Dataset and Its Baseline with Triple-domain Feature Learning
- 构建首个真实工业场景的传送带裂纹序列图像数据集
- 提出三域特征融合模型，在新数据集上显著优于现有方法
- 适合工业视觉检测与多模态学习研究者参考
传送带是现代工业中广泛使用的设备，其健康状态直接影响生产效率与安全。裂纹是威胁传送带性能的主要因素。当前，如何通过机器学习实现裂纹的智能检测日益受到关注。然而，现有裂纹数据集主要集中在道路或合成数据，缺乏真实工业场景下的传送带裂纹数据。为此，本文首次构建了两个全新的序列图像工业传送带裂纹数据集，并提出一种基于时-空-频三域特征层级融合的基准方法以验证其有效性。实验表明，所提数据集具备可用性与实用性，且该基准方法在检测性能上明显优于其他同类方法。相关数据集与代码已开源（https://github.com/UESTC-nnLab/BeltCrack）。
原文摘要 · Abstract (English)
Conveyor belts are important equipment in modern industry, widely applied in production and manufacturing. Their health is much critical to operational efficiency and safety. Cracks are a major threat to belt health. Currently, considering safety, how to intelligently detect belt cracks is catching an increasing attention. To implement the intelligent detection with machine learning, real crack samples are believed to be necessary. However, existing crack datasets primarily focus on pavement scenarios or synthetic data, no real-world industrial belt crack datasets at all. Cracks are a major threat to belt health. Furthermore, to validate usability and effectiveness, we propose a special baseline method with triple-domain ($i.e.$, time-space-frequency) feature hierarchical fusion learning for the two whole-new datasets. Experimental results demonstrate the availability and effectiveness of our dataset. Besides, they also show that our baseline is obviously superior to other similar detection methods. Our datasets and source codes are available at https://github.com/UESTC-nnLab/BeltCrack.
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