To address the problem that the limited field of view of single camera of widely deployed highway surveillance cameras makes it difficult to achieve large-scale continuous perception， a cross-camera Bird’s-Eye View （BEV） road geometric alignment task was proposed to improve scene consistency and completeness. However， this task faces challenges due to the perspective differences and structural misalignments among multi-camera images. The horizon， as a global geometric prior， can unify these perspective differences， but its detection is easily affected by occlusion and environments， limiting alignment accuracy. To solve this problem， a horizon detection method for cross-camera BEV road alignment， named RoadHoriNet （Road Horizon detection Network）， was proposed. Firstly， perspective transformation and bounding box cropping were applied for data augmentation. Secondly， a diamond space representation was introduced to alleviate instability in vanishing-point learning. Thirdly， Receptive-Field Attention Convolution （RFAConv） and upsampling by Dynamic Sampling （DySample） were used to enhance feature representation and reconstruction accuracy. Finally， a geometric consistency loss function was designed to enhance the constraints of the orientation and position of horizon detection. Experimental results demonstrate that on the BrnoCompSpeed dataset， RoadHoriNet achieves a pixel error of 5.166%， an angle error of 0.032 5°， and a detection accuracy of 94.834%， while reducing the pixel error by 4.815 percentage points and the angle error by 0.019 4° compared with the adaptive horizon detection method. In the task of cross-camera BEV road geometry alignment， the relative alignment accuracy of RoadHoriNet reaches at least 99.129% after being corrected by the RoadHoriNet method， demonstrating its practicality and generalization potential in real-world traffic environments. It can be seen that RoadHoriNet provides a stable geometric prior for camera pose normalization and multi-camera coordinate unification， improving the relative alignment accuracy and robustness of cross-camera BEV road geometric alignment significantly.