文献类型：期刊文献

英文题名：Ore image segmentation Based on Multiscale Parallel Efficient Channel Attention U-Network

作者：Wang, Xiaoli[1,2]; Feng, Mengguang[1,2]; Tang, Xiangxiang[1,2]; Peng, Tao[1,2]; Li, Zhongmei[1,2]; Yang, Chunhua[1,2]

机构：[1] School of Automation, Central South University, Changsha, 410083, China; [2] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

年份：2024

卷号：58

期号：22

起止页码：101

外文期刊名：IFAC-PapersOnLine

收录：EI(收录号：20244617356837)

语种：英文

外文关键词：Deep learning - Grinding machines - Image coding - Image enhancement - Image segmentation

摘要：Accurately detecting ore particle size and applying them to the control of semi-autogenous grinding mills are important for improving grinding productivity. Due to the wide range of ore size distribution and mutual adhesion of ores when entering the mill, the current methods based on deep learning image segmentation suffer from significant limitations and poor segmentation effectiveness in practical application. To address this issue, we propose a multi-scale parallel efficient channel attention U-net (MPECA-Unet) segmentation model. The proposed method introduces two-dimensional global attention computation in both spatial and channel domains. It also integrates multiscale fusion information with the encoder's varied scale features to improve segmentation performance for ore image. Experimental results show that the segmentation performance of the model is superior, with smaller error rate when compared to the on-site manual screening results. ? 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).