Automated Hailpad Dent Detection and Segmentation Using Machine Learning

Hailpads are a widely used method for recording hailstone impact data by capturing indentations made by hailstones during hailstorms. As a simple and low-cost approach, hailpads are often deployed in vast networks to collect spatially-distributed data. However, the subsequent process of analyzing these indentations is often long and intensive (taking up to several hours for a single hailpad), and subjective. This research presents a novel, automated approach to identify the major/minor axes and depth distributions of hailpad dents via an image processing and machine learning pipeline, aimed at reducing the time and effort required to analyze the constituent dents of a hailpad. Using high-precision 3D scans, hailpads are depth mapped and then binarized based on user-prescribed adaptive thresholding, contrast equalization, and area filtering parameters. Next, the resulting binary masks are used as input to a convolutional neural network (CNN), which separates dents in clustered and non-clustered regions via instance segmentation. Built on a training dataset of simulated hailpad binary masks, the model was evaluated with a 93.0% Intersection over Union (IoU) score on predicted dent masks. In further comparisons against manual analyses and third-party commercial 3D scan assessments, the model excels in identifying individual impacts from within densely grouped regions. However, the overall prediction distributions are hindered to varying degrees by the influence of false positives in dent detection from non-hail artifacts in the input binary masks. Overall, this automated approach demonstrates the potential to considerably expedite hailstone dent identification and lays the groundwork for extracting more physical properties in the future, such as approximations for volume, impact velocity, and accumulated impact energy.