When no obvious boundaries exist between skin regions and non-skin regions, skin detection becomes extremely difficult. To solve this problem, we propose a new skin detection and correction algorithm. Firstly, this study uses a convolutional neural network (CNN) to extract skin features such as colors and texture step by step and then subdivides the boundary region of skin and non-skin pixels through the gated convolutional layer to enhance the effect of skin detection. Finally, ASPP is applied to fuse deep information and edge information. The detection results from rough threshold segmentation are used as input for the evaluation on ECU and Pratheepan datasets. The experimental results show that the accuracy of this algorithm reaches up to 91% on the ECU dataset and 95% on the Pratheepan dataset. The performance of the proposed algorithm has been significantly improved compared with that of the existing methods.