In order to address the problems of indistinct features, time-consuming field interpretation, and low accuracy in Ground Penetrating Radar (GPR) echo image recognition, an improved method called GSR-YOLOv7 based on YOLOv7 is proposed in this study. First, GhostConv was used to replace the convolutional kernels in the YOLOv7 convolutional layers, thus reducing the number of parameters. Second, the SimAM attention mechanism was introduced to improve the feature learning capabilities. Third, the reuse of the receptive field block (RFB) convolutional kernel was implemented to enlarge the receptive field. Finally, an improved EIoU loss function was employed to improve model classification and regression accuracy. Experimental results on a dataset of road echo images demonstrate the effectiveness of the proposed approach. The GSR-YOLOv7 model achieves a MAP50 score of 97.57% and a MAP50∶95 score of 73.13%, showing improvements of 2.13% and 8.46%, respectively, over YOLOv7. In addition, the model size is reduced by 35%. The GSR-YOLOv7 model exhibits excellent detection performance for echo targets and is suitable for use on mobile systems. It has significant value for platforms with limited processing power and small form factors.