Colonoscopy is an important technical means for screening early colorectal cancer lesions. Accurate segmentation of intestinal polyps helps improve the accuracy of screening. Early screening for lesions is of great significance for the prevention of colorectal cancer, and the segmentation of intestinal polyps is an important research direction. Although intestinal polyp segmentation based on deep learning has achieved acceptable performance, the color variation among intestinal endoscopic images significantly affects it. Based on the ResNet architecture, this study proposes an advanced PE-ResNet in which histogram equalization is used to reduce color influence. Experimental results on five datasets, including ClinicDB, demonstrate that the PE-ResNet model achieves improved performance in intestinal polyp segmentation.