A Surface Crack Damage Evaluation Method Based on Kernel Density Estimation for UAV Images

The development of UAV (unmanned aerial vehicle) technology provides an ideal data source for the information extraction of surface cracks, which can be used for efficient, fast, and easy access to surface damage in mining areas. Understanding how to effectively assess the degree of development of surface cracks is a prerequisite for the reasonable development of crack management measures. However, there are still no studies that have carried out a reasonable assessment of the damage level of cracks. Given this, this article proposes a surface crack damage evaluation method based on kernel density estimation for UAV images. Firstly, the surface crack information from the UAV images is quickly and efficiently obtained based on a machine learning method, and the kernel density estimation method is used to calculate the crack density. The crack nuclear density is then used as a grading index to classify the damage degree of the study area into three levels: light damage, moderate damage, and severe damage. It is found that the proposed method can effectively extract the surface crack information in the study area with an accuracy of 0.89. The estimated bandwidth of the crack kernel density was determined to be 3 m based on existing studies on the effects of surface cracks on soil physicochemical properties and vegetation. The maximum crack density value in the study area was 316.956. The surface damage area due to cracks was 14376.75 m2. The damage grading criteria for surface cracks in the study area (light: 0–60; moderate: 60–150; severe: >150) were determined based on the samples selected from the field survey by crack management experts. The percentages of light, moderate, and severe damage areas were 72.77%, 23.22%, and 4.01%, respectively. The method proposed in this article can effectively realize the graded damage evaluation of surface cracks and provide effective data support for the management of surface cracks in mining areas.