## 1 Introduction  
Rock slope hazards, including rockfalls and landslides, account for over 70% of natural geological disasters [1, 2, 3, 4]. Rock slopes are composed of intact rock and discontinuities, with the strength of discontinuities being significantly lower than that of intact rock [5]. Consequently, the formation of rock slope hazards is primarily controlled by the strength characteristics and spatial distribution of discontinuities. On the basis of this understanding, numerous scholars have conducted research on rock slope disasters following the structural control theory of rock masses [6, 7, 8, 9]. The rock mass structure control theory involves predicting mechanical behaviors by analyzing the internal structural characteristics. The most fundamental aspect of this approach lies in acquiring information about rock discontinuities. Traditional methods employ geological compasses and survey lines to measure discontinuities within specific observation windows. However, these approaches only capture partial discontinuity information from localized areas of rock slopes, making them incomplete for comprehensive characterization. With advancements in Terrestrial Laser Scanning (TLS) and LiDAR technologies, the interpretation of discontinuity information through 3D point cloud data has become a primary method for its characterization [10]. TLS and LiDAR systems acquire three-dimensional spatial coordinates by uniformly emitting laser beams and capturing reflected signals [11]. Nevertheless, due to topographic constraints and imaging distance limitations, TLS-derived point cloud data typically exhibits centimeter-level spacing [12]. Under the recent framework of multi-scale rock mass structure analysis [13], millimeter-scale discontinuities, though not constituting controlling factors for slope instability, can still reduce rock mass strength and influence slope stability assessments [14]. Consequently, analyzing millimeter-aperture discontinuities becomes essential. However, current TLS and LiDAR methods, constrained by centimeter-level precision, remain inadequate for detecting such fine-scale discontinuities. Against this backdrop, UAV (unmanned aerial vehicle) low-altitude photogrammetric modeling has gradually emerged as a mainstream method for acquiring discontinuity information in engineering and research applications, owing to its cost-effectiveness and efficient field operations [15]. Notably, [PERSON] et al. [16] proposed a multi-angle and nap-of-the-object photogrammetric method, enabling millimeter-level precision modeling of rock slopes. This advancement provides a feasible approach for detecting millimeter-aperture discontinuities. However, higher-precision models inevitably generate massive point cloud data and discontinuity information. Relying on traditional manual interpretation methods remains time-consuming and labor-intensive, significantly hindering the efficiency of discontinuity extraction. Consequently, achieving rapid acquisition of discontinuity information from high-precision UAV models has become a critical focus for advancing rock slope hazard analysis. In recent years, UAV photogrammetry surveys equipped with GNSS antennas and RTK/PPK systems have enabled the generation of 3D models with accuracies of around 3-5 cm. This makes it possible to analyze vertical rock faces with high accuracy and supports both susceptibility assessments and mitigation strategies for rockfalls [17]. UAV-derived models not only provide point cloud data representing 3D spatial information but also integrate RGB color attributes for each point [18]. Field observations indicate that discontinuities often exhibit distinct gray-black coloration, contrasting sharply with surrounding rock surfaces. This chromatic differentiation offers a potential pathway for automated identification and interpretation of discontinuities. Building on these insights, this study proposes an intelligent discontinuity identification method leveraging RGB information from high-precision UAV models.