An optimal approach for crack extraction from UAV sub-images after cutting

ABSTRACT

Obtaining real-time, objective, and high-precision distribution information of surface cracks in mining areas is the first task for studying the development regularity of surface cracks and evaluating their risk. However, the complex surface environment causes low precision of crack extraction in the existing unmanned aerial vehicle (UAV) image methods. Therefore, we propose an extraction method for surface cracks from UAV sub-images after cutting and a new evaluation index of APS (Accuracy-Precision-Sensitivity). First, the UAV image was divided into sub-images with unit sizes of 50, 100, and 200, and the bare ground (BG) and vegetation (VT) datasets based on background characteristics were built. Then, we used the five crack extraction methods improved and proposed to process the datasets. Finally, this study determined the optimal unit size to extract the cracks and the optimal method for a certain size by comparison. The results showed that the extraction method based on the unit method was better than the complete UAV image, and the crack extraction effect of the BG dataset was significantly better than that of the VT dataset. The APS index, which comprehensively considers the accuracy, precision, and sensitivity, is obviously more reasonable than the accuracy index for evaluating crack extraction methods. Moreover, the smaller the unit size, the better the effect of crack extraction. The Hue-Saturation-Value threshold segmentation method had the best extraction effect when the size was small, and the deep learning method was the best when the size was large. We proved that the proposed method can achieve higher-precision crack extraction from UAV images and can also support data calculation in crack feature extraction.

KEY POLICY HIGHLIGHTS

• The crack extraction method based on unit method

• The APS index to evaluate the crack extraction method

• The best unit size and crack extraction method for the sub-image

KEYWORDS:

• UAV image
• crack extraction
• evaluation method
• unit method
• deep learning

Acknowledgements

Thanks to the Institute of Land Reclamation and Ecological Reconstruction and Shaanxi Coal Group for their help in this research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Some or all data, models, or code generated or used during the study are available from the corresponding author by request.

Additional information

Funding

This research was supported by The Research and Demonstration of Key Technology for Water Resources Protection and Utilization and Ecological Reconstruction in Coal Mining area of Northern Shaanxi [2018SMHKJ-A-J-03], and Monitoring on Resource and Environment and Ecological Restoration in Coal Mine Areas [42142002]. The fund [2018SMHKJ-A-J-03] is supported by coal companies, so it may not be found in Open Funder Registry.