ABSTRACT The pitch and row pitch of rock bolts are critical parameters in rock‐support techniques. These parameters facilitate tracking construction processes and monitoring bolt displacement for early warning of rock bursts and roof falls. A measurement method for these pitches based on dual‐modal image recognition is proposed to solve the problems of high personnel safety risks, low accuracy, and high labour intensity in traditional measurement. First, an intrinsically safe structured‐light camera captures colour images and point clouds, and Gaussian heatmap‐based point annotation is conducted on the colour images to produce training samples. Second, the detection network is enhanced via point feature maps, feature point attention, and mixed up‐sampling. A weighted Gaussian heatmap is adopted as the loss function to develop a rock bolt point detection network, which effectively enhances the detection and localisation accuracy. Finally, a two‐stage serial fitting method is proposed to solve the linear equation for each row and complete the measurement. Simulation experiments demonstrate that the developed point detection network achieves notable gains in precision, recall, and positioning error. The precision is 87.53%, the recall is 93.03%, and the positioning error is 11.33 mm, all ranking among the top performers. Industrial experiments demonstrate that the method is well‐suited for on‐site applications.
You et al. (Thu,) studied this question.