Application of Optimized Convolution Neural Network Model in Mural Segmentation

Abstract

To address the problems of blurred target boundaries and inefficient image segmentation in ancient mural image segmentation, a multi-classification image segmentation model MC-DM (Multi-class DeeplabV3+ MobileNetV2) that fuses lightweight convolutional neural networks is proposed. The model combines the Deeplabv3+ structure and MobileNetV2 network and adopts the unique spatial pyramid structure of DeeplabV3+ to process convolutional features for multi-scale fusion, which reduces the loss of detail in the mural segmentation images. Firstly, the features calculated at any resolution in MobileNetV2 network are extracted by hole convolution, the input step is expressed as the ratio between the input image resolution and the final resolution, and the density of encoder features is controlled according to the budget of computing resources. Then, the spatial pyramid pool structure is used to fuse the previously calculated features at multiple scales to enrich the semantic information of the feature image. Finally, the same convolution network is used to reduce the number of channels and filter the density feature map. The filtered features are fused with the features after multi-scale fusion again to obtain the final output. In total, 1000 scanned images of murals were adopted as datasets for testing under the JetBrains PyCharm Community Edition 2019 environment. The obtained experimental results indicate that MC-DM improves the training accuracy by 1 percentage point compared with the conventional SegNet-based image segmentation model, and by 2 percentage points compared with the PspNet network-based image segmentation model. The PSNR (peak signal-to-noise ratio) of the MC-DM model is improved by 3–8 dB on average compared with the experimental model. This confirms the effectiveness of the model in mural segmentation and provides a novel method for ancient mural image segmentation.