Electrical impedance tomography (EIT)provides a feasible method for visualizing the distribution of pressure points in flexible tactile sensors of robots due to its non-invasive characteristics.However,the inverse problem of EIT is highly nonlinear and ill-posed.When multiple pressure points are close to each other,the artifacts of the reconstructed image will lead to adhesion between pressure points.To solve the above problems,a post-processing algorithm named S- PNet for EIT is proposed,which is composed of three modules:Feature extraction,feature reconstruction and enhanced feature extraction,to achieve the segmentation and shape reconstruction of adhesive pressure points.The algorithm uses a pyramid pooling structure to enhance feature extraction and can extract multi-scale features to distinguish the boundaries of close pressure points with minimal additional calculation.Thepost-processing image quality is evaluated by root mean square error (RMSE)and structural similarity(SSIM).The average value of RMSE is 0.02 and the average value of SSIM is 0.97.Both simulation and measured results show that compared with the existing algorithms,the post- processing algorithm based on S-PNet canobtain images with clear boundaries and accurate shapes.