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 de-tection 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 out-comes reveal that,under specified conditions,the model achieves a 98.39%accuracy rate,an 84.72%re-call rate,and an average recognition accuracy of 99.66%.This sound-vibration detection method effec-tively 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.
维普
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