Abstract: Addressing the issues of traditional ground penetrating radar （GPR） pipeline object detection methods， such as the inability to precisely locate pipelines， time-consuming and inefficient interpretation processes， and interference from complex background noise， this paper designs a GPR image pipeline object detection method based on YOLOv8， with improvements made to the original YOLOv8 network. First， the PConv operator is introduced into the backbone network to make the network structure more lightweight， speeding up the model’s processing speed， and reducing redundant computations and memory access. Second， the Triplet Attention module is introduced to enhance the model’s feature extraction ability across different dimensions， improving object detection accuracy in complex backgrounds. Lastly， the bounding box loss function is replaced with Wise-IoU to improve the regression performance and robustness of the bounding boxes. This paper conducts experiments using a GPR pipeline dataset， and the results show that the improved model proposed in this paper achieves better performance in terms of detection accuracy and computational cost.

Key words:  , ground penetrating radar, object detection, deep learning, YOLO, attention mechanism