A strategy for locating and tracking pipeline inspection gauge (PIG) in the pipeline via phase-sen sitive optical time domain reflectometry (φ-OTDR) and you only look once v5 (YOLOv5) target detection a lgorithm is proposed.Vibration will be generated when the cups at both ends of the PIG collide with the welding seam of the pipeline.The φ-OTDR technology ca n be used to collect the vibration signal and present an "inverted V" feature th at distinguishes it from other background noises on the space-time map.A tr aining set and a test set are constructed by obtaining a large number of space-time maps containing "inverted V" features.The training set is used to train the YOLOv5 network model,and the test set is used to test the trained YOLOv5 n etwork.The trained model is proved to be able to accurately capture the "inverted -V" feature in the space-time map,thereby inverting the real-time position an d p ath of the PIG.The distributed optical fiber sensor and neural network algorith m are combined to further improve the convenience and accuracy of PIG positionin g and tracking,which is conducive to the realization of PIG online and automati c tracking.