To address the issues of low accuracy and slow speed in extracting keyframes from highway tunnel videos,this study proposes an efficient extraction algorithm for crack keyframe extraction,inte-grating Multi-Scale Filtering(MSF)and Ridge Change Detection(RCD).First,a crack ridge feature extraction method is developed based on multi-scale filtering and the Hessian matrix,considering the gradient and second-order derivative properties of cracks across various directions and scales.Using ei-genvalue computation and threshold analysis,ridge lines from different scale filtering results are ex-tracted and fused,enabling precise extraction of crack ridge features in highway tunnel videos.Then,an indexing spatial model for road crack video frames is proposed.By employing ridge differential analysis and inter-frame dissimilarity constraints,a crack keyframe indexing mechanism is con-structed.Dynamic characteristics of crack regions are identified through ridge change detection,while representative keyframes are selected using inter-frame dissimilarity discrimination.This approach re-duces redundant frames and significantly improves the efficiency of video processing for crack detec-tion.Finally,experiments are conducted to extract keyframes from highway tunnel crack videos.The experimental results demonstrate that the proposed method improves accuracy by 19.3%to 43.2%compared to existing keyframe extraction methods,with average keyframe extraction speeds being 11 to 13 times faster than motion-based methods.This method effectively enhances highway tunnel crack detection efficiency and provides valuable insights for intelligent crack detection in highway tunnels.
维普
万方数据
