Topographically Guided UAV for Identifying Tension Cracks Using Image-Based Analytics in Open-Pit Mines

Aerial imaging of an open-pit mine integrated with the visual analytics offers a novel approach for routine monitoring of tension cracks for mine safety. Tension cracks may occur on work- or catch-benches that are excavated according to a computer aided design (CAD) model. The size of the tension cracks, their locations, and evolutions is commonly used to predict slope failures and to assure the mine safety operations. The goal of this research was to replace the current manual interventions with an automated platform for routine report generations for the mine controller. First, a drone was flown on a preprogrammed flight trajectory at a constant elevation to generate a mosaic and a depth map image. Next, work-, catch-benches, and access roads were automatically identified and represented by their medial axes. Subsequently, the waypoints from each medial axis were sequentially uploaded into the drone for scanning the corresponding regions at high-resolution. These high-resolution images were then used to delineate tension cracks. The delineation of tension cracks was performed using steerable filters, ENet, and UNet deep learning models for comparison. The ENet model, with the leave-one-out cross-validation method, produced the best performance profile with an Aggregated Jaccard Index and F1-Score of 0.51 and 0.79, respectively.

Automated summary

Aerial imaging integrated with visual analytics provides a novel approach for routine monitoring of tension cracks in an open-pit mine to ensure mine safety. The research aimed to automate report generation for the mine controller by using drone flights to capture high-resolution images for delineating tension cracks. Deep learning models like ENet showed promising performance in accurately detecting tension cracks with an Aggregated Jaccard Index and F1-Score of 0.51 and 0.79, using a leave-one-out cross-validation method.