A novel sound-vibration detection approach， leveraging a target detection algorithm， merges acoustic stimulation， laser speckle interferometry， and target detection algorithms for efficient and broad-range detection of flexible， shallowly buried objects. Initially， after discussing the YOLO series target detection algorithm principles， an optimal intelligent detection network model for these objects is chosen. Subsequently， a sound-light fusion intelligent detection system is developed， creating a dataset of laser speckle interference patterns for various flexible， shallowly buried objects. This dataset is then trained and tested to evaluate the algorithm's effectiveness in recognizing interference patterns. Experimental outcomes reveal that， under specified conditions， the model achieves a 98.39% accuracy rate， an 84.72% recall rate， and an average recognition accuracy of 99.66%. This sound-vibration detection method effectively identifies laser speckle interference patterns of numerous flexible， shallowly buried objects in the tested environment， proving its efficacy for quick， large-scale detection of such objects underground.