Geometry-Guided Monocular Vision Measurement of Steel-Strip Anchor-Hole Localization for Roadway Support Drilling in Low-Visibility Coal Mine Environments

Accurate measurement of steel-strip anchor holes is crucial for intelligent roadway support drilling in underground coal mines, where severe illumination degradation, airborne dust scattering, and weak structural textures often lead to unreliable visual perception and drilling collision risks. To address these challenges, a geometry-guided monocular vision framework is proposed for anchor-hole segmentation and three-dimensional localization under low-visibility conditions. First, a physics-inspired image enhancement module combining atmospheric scattering correction and adaptive illumination compensation is developed to improve image quality. Second, an ellipse-constrained geometry-aware U-Net (EGU-Net) is proposed by embedding elliptical shape priors into feature learning and boundary refinement for robust anchor-hole segmentation. Third, an ellipse fitting-based monocular geometric reconstruction model is established to recover the three-dimensional coordinates of anchor-hole centers from calibrated imaging parameters and steel-strip planar constraints. Moreover, an analytical uncertainty propagation model is derived to quantify the influence of segmentation errors and calibration uncertainty on localization accuracy. Experimental results on a self-built underground support drilling dataset demonstrate that the proposed method achieves an IoU of 94.33% and a localization MAE of 3.27 mm, outperforming existing approaches and exhibiting strong robustness under low-visibility environments. The proposed framework provides an effective visual measurement solution for intelligent and safe roadway support drilling.