基于CBAM-ResNet50的金刚石颗粒净度检测方法  

Diamond particle clarity detection method based on CBAM-ResNet50

作　　者：费文倩 赵凤霞[1] 杜全斌 王庆海[2] FEI Wenqian;ZHAO Fengxia;DU Quanbin;WANG Qinghai(School of Mechanical and Power Engineering,Zhengzhou University,Zhengzhou 450001,China;School of Intelligent Engineering,Henan Mechanical and Electrical Vocational College,Zhengzhou 451191,China)

机构地区：[1]郑州大学机械与动力工程学院,郑州450001 [2]河南机电职业学院智能工程学院,郑州451191

出　　处：《金刚石与磨料磨具工程》2024年第5期588-598,共11页Diamond & Abrasives Engineering

基　　金：河南省超硬材料智能制造装备集成重点实验室开放课题(JDKJ2022-03)。

摘　　要：针对金刚石颗粒净度传统检测方法效率低、准确率差的问题,提出了一种基于迁移学习和改进Res-Net50的金刚石颗粒净度检测算法CBAM-ResNet50。该算法通过在ResNet50主干网络的每层中增加CBAM,以提升模型特征的提取能力;且在主干网络的Layer3和Layer4中融入FPN结构,对提取的特征进行部分特征聚合,来解决采样过程中小目标特征易丢失的问题;同时引入迁移学习方法,用交叉熵损失函数优化模型的初始参数,提升模型的泛化能力。结果表明:在学习率设置为0.0001时,提出的CBAM-ResNet50模型训练准确率达到99.2%;根据混淆矩阵计算得到模型的精确度在99.20%以上,特异性在99.70以上%,F1分数在99.20%,分类召回率在98.70%以上,优于其他主流分类网络的结果,有效提高了金刚石颗粒净度检测的识别能力。Objectives:With the improvement of production technology,the traditional diamond particle cleanliness detection method can no longer meet the requirements of high precision,high quality and high automation in the diamond industry due to its low efficiency and poor accuracy.The rapid development of computer technology,optical,and electronic technologies has led to the widespread application of visual inspection and deep learning in image classification and detection,providing new methods for diamond clarity detection.Therefore,based on transfer learning and combined with the convolutional block attention module(CBAM)attention mechanism and the feature pyramid network(FPN)structure,an improved ResNet50 diamond particle clarity detection algorithm,CBAM-ResNet50,is proposed.Methods:The CBAM-RESnet50 clarity detection algorithm uses ResNet50 as the backbone network and adds CBAM to each layer of the backbone network to improve the feature extraction ability of the model.In addition,the FPN structure is integrated into Layer 3 and Layer 4 of the backbone network,where part of the extracted features are aggregated to address the issues of losing features of small and medium-sized targets during the sampling process.At the same time,the transfer learning method is introduced to optimize the model's initial parameters with a cross-entropy loss function,thereby improving the generalization ability and robustness of the model.Moreover,multi-angle diamond images are collected on a diamond clarity detection device,a diamond particle clarity dataset is established,and the improved CBAM-ResNet50 network model is experimentally compared and verified using the data set.Results:Firstly,when compared with other classic mainstream network models,the accuracy of the CBAM-ResNet50 model during training is 99.2%,and the precision is 99.7%,ourperforming the classification results of other network models and significantly improving the identification ability for diamond particle clarity detection.The average detection time of the CBAM-Re

关 键 词：金刚石净度 ResNet50 卷积块注意力模块 特征金字塔网络 迁移学习 

分 类 号：TQ164[化学工程—高温制品工业] TP391.4[自动化与计算机技术—计算机应用技术]