Research on cuttings image segmentation method based on improved MultiRes-Unet++ with attention mechanism

Abstract Cuttings logging is an important technology in petroleum exploration and production. It can be used to identify rock types, oil and gas properties, and reservoir features. However, the cuttings collected during cuttings logging are often small and few. Meanwhile, the surface color of cuttings is dark and the boundary is fuzzy. Traditional image segmentation methods have low accuracy. So it is difficult to identify and classify cuttings. Therefore, it is important to improve the accuracy of cuttings image segmentation. A deep learning-based cuttings image segmentation method is proposed in this paper. Firstly, the MultiRes module concept based on the U-Net + + segmentation model is introduced in this paper, which proposes an improved end-to-end U-Net + + image semantic segmentation model (called MultiRes-UNet++). Secondly, batch normalization (BN) into the input part of each layer's feature convolution layer is introduced too. Finally, a convolutional attention mechanism in the improved MultiRes-UNet + + segmentation model is introduced. Experimental results show that the accuracy (ACC) between the segmentation results and the original image labels is 0.8791, the dice coefficient (DICE) value is 0.8785, and the intersection over union (IOU) is 0.7833. Compared with existing neural network segmentation algorithms, the performance is improved by about 5%. Compared with the algorithm before the fusion of the attention mechanism, the training speed is increased by about 75.2%. Our method can provide auxiliary information for cuttings logging. It is also of great significance for subsequent rock identification and classification.