Research on the PDC rock cuttings image instance segmentation method based on improved U-Net plus plus network and multi-feature fusion

Currently, PDC bits dominate the petroleum bit market. It is a small cut of polycrystalline diamond, which is embedded in the body of the drill bit. Due to the small number of cuttings generated through the PDC bit, the individual cuttings are small, and the cuttings edge is blurred, which results in poor effect and low precision of traditional image segmentation. Therefore, this paper proposes an image segmentation method regarding deep learning. Firstly, a multi-task learning method is introduced based on the U-Net segmentation model. A multi-task-learning-U-Net++ instance segmentation model is proposed. The results of semantic segmentation and edge segmentation are obtained by this segmentation model. Then, the cuttings are segmented by superpixel to obtain the sub-blocks of superpixel. Finally, a multi-feature fusion method is proposed, which integrates semantic information, edge information and superpixel sub-blocks to obtain the segmentation result of cuttings image. Experimental results show that the proposed method can effectively segment each cutting in the image and this method has certain robustness. Compared with the segmentation algorithms such as U-Net and U-Net++, this algorithm performs better in multiple image segmentation performance indexes. The small number of cuttings generated through the PDC bit, the individual cuttings are small and the cuttings edge is blurred, which results in poor effect and low precision of traditional image segmentation.The image segmentation method based on deep learning is more suitable to solve the segmentation problem of cuttings images under PDC conditions.A multi-task learning method is introduced based on the U-Net++ segmentation model.A multi-feature fusion method is proposed, which integrates semantic information, edge information and superpixel sub-blocks to obtain the segmentation result of cuttings image.