Intelligent recognition of drill cores and automatic RQD analytics based on deep learning

Abstract

Rock quality designation (RQD), as a well-accepted and appliable rock quality index, is crucial in geotechnical engineering. Current RQD estimation mainly relies on either manual statistics or the image binarisation method, while the former approach surrenders high labour intensity and low efficiency and the latter one is constrained by image acquisition. Considering the above-mentioned limitations in RQD estimation, this study proposed a novel convolutional neural network (CNN) approach to automatically perform core recognition and RQD cataloguing with significant improvement in accuracy and efficiency. Firstly, the proposed neural network automatically identified the prefabricated round markers to distinct drilling rounds. To maximumly strengthen the engineering capability of CNN without losing generality, we considered image inversion, rotation, noise addition, and RGB conversion of 200 core box samples in total. Secondly, replacing the unstable image binarisation method, the advanced YOLO V2 object detection model, a single-stage real-time object detection model, was adopted in this study. We also proposed the modified four-layer downsampling structure as our CNN, and then developed an automatic recognition approach for both cores and the round markers, resulting in a 93.1% accuracy according to the validation set. Thirdly, this study proposed an auto-ranking algorithm to sequence the core sample according to the confidence of core recognition by the CNN and row-scanning results for subsequent RQD cataloguing. In addition, the optimal scan width was proved to be 1.33 times larger than the average core width. Finally, a quick cataloguing platform for drill cores was developed. Compared with manual measurement and visual statistics, intelligent RQD cataloguing is characterised by its unparalleled accuracy and efficiency, which is merited by the low relative error (1.84%) and fast processing time (around 0.2 s). Moreover, the application presented in this paper is applicable to most geotechnical engineering scenarios. This is attributed to its low requirements in image acquisition, high efficiency, precise recognition, and robustness.