Abstract: Oblique photogrammetry with unmanned aerial vehicles（UAVs） is an essential tool for fine-scale investigation of slope geological structures. However, traditional flight route planning methods struggle to balance efficiency and accuracy, limiting the effective acquisition of detailed geological information such as joints and fractures. To address this, this paper proposes a close-range photogrammetric path optimization method that integrates slope segmentation with an improved genetic algorithm. The method takes a low-resolution initial point cloud model as input. First, through slope segmentation and classification, aerial survey resources are precisely focused on steep areas where geological structures are developed. Subsequently, fine-scale flight routes that closely follow the surface and enable multi-angle intersections are planned for each key slope to collect high-resolution images. Finally, a Traveling Salesman Problem（TSP） model incorporating directional selection is constructed, and an improved genetic algorithm is employed to solve for a relatively optimal splicing path for multi-unit flight routes. This achieves an efficient transition from a coarse model to fine data. Validation is conducted in an experimental area with dense geological structures within the mining pit of the Baorixile Open-Pit Coal Mine. The results show that while ensuring centimeter-level image resolution in key areas, the total length of the optimized survey path based on the DJI M3E UAV platform is reduced by 90% compared to traditional terrain-following flights. This significantly enhances the efficiency of identifying geological structure information and provides a reliable technical solution for detailed slope investigation in open-pit mines.