In the process of sinter production, the pores formed in the sinter are an important parameter for sinter quality evaluation. Due to the different shapes of sinter pores and their fuzzy edge, the error rates of pores segmented are large. In order that the pores can be segmented more accurately, the sinter image is preprocessed by OpenCV. Through a comparative analysis of the traditional image segmentation algorithm, this study proposes an algorithm based on an improved UNet network to segment the preprocessed sinter pore image. An improved module integrating residual and splicing is introduced into UNet network coding to obtain more information on pore features. The experimental results show that the improved algorithm is better than both the traditional UNet network and the traditional image segmentation algorithm in mean intersection over union (MIoU) and the Dice index.