BeltCrack: the First Sequential-image Industrial Conveyor Belt Crack Detection Dataset and Its Baseline with Triple-domain Feature Learning
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 24 of its 55 references that are in the map. First posted on arXiv 22 Jun 2025.
Builds on
24 of 55 references.
References · 24
- 2017Attention Is All You Needcited by 60,361
- 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,071
- 2017Focal Loss for Dense Object Detectioncited by 752
- 2019Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detectioncited by 80
- 2023PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flowcited by 74
- 2024Wavelet Convolutions for Large Receptive Fieldscited by 74
- 2024TAPTR: Tracking Any Point with Transformers as Detectioncited by 45
- 2024vid-TLDR: Training Free Token merging for Light-weight Video Transformercited by 41
- 2024Frequency-Adaptive Dilated Convolution for Semantic Segmentationcited by 34
- 2021RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depthcited by 33
- 2023YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detectioncited by 33
- 2023An Incremental Unified Framework for Small Defect Inspectioncited by 25
12 more
- 2022CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworkscited by 22
- 2022Scalable Video Object Segmentation with Identification Mechanismcited by 18
- 2024Triple-domain Feature Learning with Frequency-aware Memory Enhancement for Moving Infrared Small Target Detectioncited by 15
- 2024CrackNex: a Few-shot Low-light Crack Segmentation Model Based on Retinex Theory for UAV Inspectionscited by 12
- 2024Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priorscited by 11
- 2023Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulationcited by 10
- 2023A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentationcited by 8
- 2025Towards Accurate Unified Anomaly Segmentationcited by 7
- 2022Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identificationcited by 6
- 2024Staircase Cascaded Fusion of Lightweight Local Pattern Recognition and Long-Range Dependencies for Structural Crack Segmentationcited by 6
- 2024Adaptive Deviation Learning for Visual Anomaly Detection with Data Contaminationcited by 4
- 2015Fast Detection of Curved Edges at Low SNRcited by 3
Built on by
Citing papers per year: 2026: 2.
2 papers.