1-D MA TransUnet: A Pulse-by-Pulse Target Detection Model for Ground Penetrating Radar

The target detection task of ground penetrating radar (GPR) based on deep learning has received widespread attention. Previous studies focused more on the features of targets in images and achieved excellent performance. However, in the practical application of these methods, GPR image is cut into several slices for feeding to the model for training and inference, which not only requires accumulating pulses but disrupts the continuity of pulse information, making it difficult to communicate semantic information of different parts of the same pulse in different slices. To address these issues, this letter proposes a pulse-by-pulse target detection model, namely, 1-D mix attention (MA) TransUnet, for GPR, avoiding pulse accumulation and preserving the continuity of pulse information. In structure, the spatial and channel mixed attention mechanism replaces skip connections in 1-D Unet, which effectively enhances the target features in pulse data. In addition, transformer block (TB) based on multihead self-attention (MSA) is applied to the downsampling feature map of 1-D Unet, which allows the model to effectively understand the global semantic information and suppress nontarget features that are similar to the target features in pulse data. Finally, the effectiveness of 1-D MA TransUnet is validated using GPR pulse data containing steel mesh as a case study. The model achieved an accuracy of 83.07%, a recall rate of 71.64%, and an F1-score of 76.93%, respectively.