Abstract:During petroleum exploration, core particles are effective data for studying geological sequence, evaluating oil and gas contents, and understanding geological structures. The extraction of core particle images is conducive to the further analysis of geological researchers. The core particle images usually have blurred particle edges, and complex backgrounds and particle colors. To improve the extraction effect of core particles, this study designs a core image particle extraction algorithm based on the improved UNet3+. This algorithm adds the receptive field module (RFB) after each coding layer of UNet3+ to expand the receptive field of the network, thus solving the low segmentation accuracy caused by the limited receptive field of the network. Meanwhile, the convolutional block attention module (CBAM) is embedded after the RFB module to make the network focus on the target region more accurately and improve the feature weight of the target region. The experimental results show that compared with the original UNet3+ network, the improved algorithm yields a good segmentation effect on the core particle images, improving mIoU, mPA, and FWIoU by 5.43%, 2.99%, and 5.34%, respectively.