［Purposes ］ This study aims to address the issues of inconsistent target scale and insufficient global feature modeling in road crack detection，proposing a road crack identification method based on binocular bird ’s eye view （BEV） and an improved YOLOv 8 model to achieve efficient and accurate crack detection and segmentation.［Methods］ High -precision BEV images were generated using binocular stereo vision and inverse perspective mapping （IPM） technology to resolve the scale inconsistency problem in traditional perspectives.The proposed C 2f-DRR module captured multi -scale contextual information of cracks effectively by employing a region -residual and semantic -residual decoupling strategy.It combined large kernel convolutions with small kernel dilated convolutions to enrich image detail and reduce background interference.Additionally，a context anchor point attention mechanism was introduced to dynamically focus the model on the central region of cracks and achieve model long -range dependencies between distant pixels.［Findings］ To verify the effectiveness of the improved model，comparative experiments are conducted on the test set.The improved model achieves a mean average precision （MAP50） of 83.7%，accuracy （P） of 83.9%，and F1 score of 83.5%，with improvements of 4.4，4.0，and 1.8 percentage points，respectively，over the original YOLOv 8n model.The model is also tested on the publicly available UAV -PDD 2023 dataset，achieving an MAP50 of 70.5%，recall （R） of 64.8%，and accuracy （P） of 74.1%，with improvements of 3.5，4.5，and 0.6 percentage points，respectively.The improved model outperforms the original model in identification accuracy，robustness，and generalization ability.［Conclusions ］ The proposed BEV -based crack segmentation method effectively enhances detection accuracy and generalization in complex road environments，providing reliable technical support for automated road damage detection.