基于计算机视觉和深度学习的古桥裂缝识别方法OA

To enhance the accuracy and efficiency of crack detection of ancient bridges and address the issues of information loss and secondary damage caused by traditional sensor detection methods,a crack identification and measurement method was proposed based on an improved You Only Look Once 11(YOLO11)and SegFormer.First,to overcome the limitations of the YOLO11 model,including its large parameter size and restricted inference speed,the You Only Look Once-crack detect(YOLO-CD)object detection model was introduced.The StarNet lightweight backbone network was employed to reduce computational costs.The HSANet neck network was integrated to enhance the ability to preserve the crack edge detail,and an optimized spatial context detection(OSCD)head was designed to improve multi-scale detection efficiency.Second,an enhanced SegFormer-HF semantic segmentation model was proposed,which incorporated a feature fusion module(FFM)and a high-low frequency decomposition block(HLFDB)to mitigate information loss during sampling and improve semantic consistency in crack segmentation.Finally,a joint detection-segmentation framework was developed,combining a skeleton line algorithm to achieve automatic calculations of crack length and width.Based on the experiments conducted on the crack dataset of ancient bridges,the results have demonstrated that the YOLO-CD model achieves F1 score,mAP50,and mAP50-95 values of 0.678,0.715,and 0.464,respectively,while reducing floating-point operations(GFLOPs)by 47.62％compared to YOLO11.The SegFormer-HF model achieves superior performance with F1-score,mIoU,and mPA of 0.915,0.852,and 0.905,respectively,outperforming existing mainstream models.The results validate that the proposed method achieves higher efficiency and compact model size while balancing detection speed and accuracy,which is suitable for deployment on mobile devices such as cameras and drones.