A CNN Architecture for Discontinuity Determination of Rock Masses With Close Range Images

Abstract

Abstract. Determination of discontinuities in rock mass requires scan-line surveys performed in in-situ that can reach up to dangerous and challenging dimensions. With the development of novel technological equipments and algorithms, the studies related to rock mass discontinuity determination remain up-to-date. Depending on the development of the Structure from Motion (SfM) method in the field of close-range photogrammetry, low-cost cameras can be used to produce 3D models of rock masses. However, the determination of rock mass discontinuity parameters must still be carried out manually on these models. Within the scope of this study, a Convolutional Neural Network (CNN) architecture is proposed to identify the discontinuities automatically as the first step for fully automated processing. The Kızılcahamam/Güvem Basalt Columns Geosite near Ankara, Turkey was determined as the study area. The orthophoto of this study area was produced using close-range photogrammetric methods and the training data was produced by manual mensuration. The dataset consists of labeled binary masks and images containing corresponding Red-Green-Blue (RGB) bands. Furthermore, the amount of data was increased by applying augmentation methods to the dataset. The U-Net architecture was used to detect rock discontinuities based on the produced orthophoto. The preliminary results presented here reveal that the discontinuity determination capability of the proposed method is high based on the visual assessments, while problems exist with image quality and discontinuity identification. In addition, considering the small size of the training dataset, the accuracy of the model would increase when a larger dataset could be employed.

A Novel Rock Mass Discontinuity Detection Approach with CNNs and Multi-View Image Augmentation

“…Yet, manual interpretation of the data from scan-line surveys to detect and measure discontinuities in rock masses has the major drawbacks of requiring expertise and being time-consuming. On the other hand, while deep learning methods, particularly CNNs for image segmentation and classification, provide promising results for discontinuity detection (see Yalcin et al [27,28]), they are also limited by the manual labeling required to obtain the necessary amount of data for learning the model parameters. Data augmentation techniques and transfer learning approaches can help overcome this obstacle.…”

“…However, due to the nature of the problem, the identified discontinuities contain a high level of noise sourced from topographic variations and the presence of different textures on rock surfaces [25]. Convolutional neural network (CNN) architectures have been widely used for various image processing, feature extraction, and object segmentation tasks (e.g., see Qiu et al [26] and Yalcin et al [27][28][29][30]), including edge detection. However, they require large amounts of data and computational resources for model training.…”

“…However, they require large amounts of data and computational resources for model training. Yalcin et al [27,28] detected rock discontinuities on orthophotos using a CNN, and they emphasized that blurring was observed at the discontinuities due to the inferior quality of the digital surface model (DSM), especially at locations with poor illumination and shadow. On the other hand, detecting discontinuities in geometrically unprocessed (raw) images requires the use of multiple images with different viewing angles to obtain the 3D position information and ensure model completeness.…”

“…Domain adaptation, a specific type of transfer learning, occurs when related tasks have different data distributions [32]. We investigated the domain adaptation of CNNs pre-trained with crack data in our process as these approaches have recently proven to be successful in increasing model performance with a small amount of data being used for fine tuning (Yalcin et al [27,28,30]). A practical application was carried out on basalts in Bala town of Ankara using data acquired with an RPAS.…”

“…Thus, instead of training a CNN model from a single orthoimage, all 34 raw images were used in the model training, which is called multi-view or alternatively 3D augmentation here. Another advantage of working with the original (raw) images is eliminating the radiometric and geometric issues in orthophotos caused by DSM quality, which is usually poor at the discontinuities due to shadows and illumination problems, as explained by Yalcin et al [27]. The photogrammetric back projection used for the 3D augmentation method is based on collinearity equations (Equations ( 1) and (2)) [44].…”