## 1 Introduction  
It is necessary to investigate the stability characteristics of rock mass structures in various engineering applications, such as geotechnical engineering, geological engineering, hydropower engineering, petroleum development and geological disaster prevention and control engineering [1, 2, 3]. The condition of structural planes such as joints, faults, weak surfaces, and layers in rock masses is very important for determining the stability of rock masses in practical engineering [4, 5, 6]. The digital panoramic borehole imaging system can obtain high-precision panoramic images of the internal structural planes of the borehole by using the borehole camera technique [2, 7, 8]. These high-precision borehole images can accurately record the characteristics of the structural planes of the rock masses [9, 10, 11]. In engineering practice, the accurate identification of the structural planes in the panoramic borehole images and the rapid extraction of characteristic parameters are of critical significance for engineering exploration, engineering design and construction [12, 13, 14, 15]. The development of Artificial Intelligence (AI) and the Internet of Things (IoT), machine-learning approaches [16] and big-data techniques [17] provides novel ways to process borehole video images with the help of multimedia tools [18]. However, there is a long road to realizing the actual application of such techniques in drilling engineering and borehole detection. At present, recognition of the structural planes of a panoramic borehole image is mainly done via manual interpretation. Generally, technicians subjectively define the sine curve control points or other necessary characteristic parameters of a structural plane. A computer performs preprocessing or characteristic curve fitting and then calculates the fitting parameters to identify and extract the structural plane parameters in a borehole image [19; 20; 21; 22; 23]. This process often requires human intervention and is cumbersome and slow to operate. In addition, different interpretation technicians may produce different recognition results, which leads to non-unique conclusions on the interpretation of rock-mass structural characteristics [24; 25]. In practical engineering, especially in the field of panoramic borehole image recognition of deep hole or multi-hole detection, this manual identification process is a huge workload, involving repetitive operations. It is very time-consuming and laborious [26; 27]. Therefore, the automatic recognition technology of panoramic borehole images with no or little human intervention is particularly vital, and can improve the value of drilling engineering. Therefore, many scholars have improved the automatic process of borehole images, such as image color space transformation and Hough transformation for automatic interpretation of structural planes [28]. [PERSON] et al. attempted to use borehole image automatic recognition technology to realize the division of lithology and sedimentary facies in reef-shoal reservoirs [29]. [PERSON] et al. realized the automatic detection of the plane features of borehole images through gradient boundary detection and sinusoidal retrieval methods [30]. [PERSON] et al. used methods, such as multi-resolution texture segmentation and pattern recognition methods based on a Gabor filter, to achieve accurate interpretation of fracture planes in borehole TV images [31]. [PERSON] et al. developed a relatively independent Borehole and Ice Feature Annotation Tool (BIFAT) software platform based on borehole images [32]. These researchers and contributions have effectively promoted automatic recognition technology for borehole images and laid the foundation for the automatic recognition of full-hole panoramic borehole images.