In order to achieve the goal of quantitative application, high-precision cloud detection has become one of the key steps in remote sensing data preprocessing. However, traditional cloud detection methods have the disadvantages of complex features, multiple algorithm steps, poor robustness, inability to combine high-level features with low-level features, and ordinary detection performance. In view of the above problems, this paper proposes a high-precision cloud detection method based on deep residual fully convolutional network, which can achieve the target pixel level segmentation of cloud layer in remote sensing images. First, the encoder extracts the deep features of the image through continuous down-sampling of the residual module. Then, the bilinear interpolation is used for sampling, and the decoding is completed by combining the image features after multilevel coding. Finally, the decoded feature map is fused with the input image and convolution is performed again to achieve end-to-end cloud detection. Experimental results show that, in terms of the Landsat 8 cloud detection data set, the pixel accuracy of the proposed method reaches 93.33%, which is 2.29% higher than that of the original U-Net, and 7.78% higher than that of the traditional Otsu method. This method can provide useful reference for research on intelligent detection of cloud targets.