基于改进UNet3+的岩心图像颗粒提取算法

在石油勘探过程中, 岩心颗粒是研究地质层序、评估油气含量以及认识地质构造的有效资料, 对岩心颗粒图像进行颗粒提取有利于地质研究人员后续的深入分析. 岩心颗粒图像通常存在颗粒边缘模糊、背景与颗粒色彩复杂的问题. 为了改善岩心颗粒提取的效果, 本文设计了一种基于改进UNet3+的岩心图像颗粒提取算法. 该算法在UNet3+的每个编码层后加入感受野模块(RFB)来扩大网络的感受野, 从而有效地解决网络因感受野受限而导致的分割精度低的问题, 并在RFB模块后嵌入了卷积块注意力模块(CBAM)使网络更加精确地聚焦于目标区域, 提高目标区域的特征权重. 实验结果表明, 改进后的算法在岩心颗粒图像上具有良好的分割效果, 相比原始UNet3+网络, 分别在mIoU、mPA和FWIoU上提升了5.43%、2.99%和5.34%.

Core Image Particle Extraction Algorithm Based on Improved UNet3+

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.