[1]吕璐璐,陈树越,王利平,等.水体微纤维图像识别的改进MobileNetV2算法[J].郑州大学学报(工学版),2021,42(05):25-31.
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水体微纤维图像识别的改进MobileNetV2算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
42
期数:
2021年05期
页码:
25-31
栏目:
出版日期:
2021-09-10

文章信息/Info

Title:
MobileNetV2 Algorithm for Water Microfiber Image Recognition
作者:
吕璐璐陈树越王利平许霞
文献标志码:
A
摘要:
针对目前人工识别水体微纤维耗时耗力和传统图像处理算法鲁棒性弱等问题,构建了一种改进MobileNetV2网络识别微纤维算法。在特征提取部分采用特征重构策略,压缩深度卷积特征,提取全局卷积特征信息,利用多层全连接学习深度卷积特征的重要性并减少网络参数的计算,然后分配权重完成微纤维特征的重构。此外,采用不同大小的下采样器捕获不同尺度的特征信息并融合,增强微纤维图像的关键信息,提升模型对微纤维图像的学习能力与识别微纤维的效果。实验结果表明,改进MobileNetV2网络的微纤维识别准确率达到0.9796,与原始MobileNetV2网络相比较,识别准确率提高了0.0254,同时,误识率和漏识率也有显著的降低。
Abstract:
Aiming at the current time-consuming and labor-consuming manual identification of water microfibers and the weak robustness of traditional image processing methods, an improved MobileNetV2 network identification method for microfibers was constructed. In the feature extraction part, the feature reconstruction strategy is used to compress deep convolution features, extract global convolution feature information, use multi-la<x>yer full connection to learn the importance of deep convolution features and reduce the calculation of network parameters, and then assign weights to complete microfiber features Refactoring. In addition, different sizes of downsamplers are used to capture and fuse feature information of different scales to enhance the key information of microfiber images, and improve the models ability to learn microfiber images and the effect of identifying microfibers. Experimental results show that the improved MobileNetV2 networks microfiber recognition accuracy rate reaches 0.9796. Compared with the original MobileNetV2 network, the recognition accuracy rate is increased by 0.0254. At the same time, the false recognition rate and the missed recognition rate are also significantly reduced.
更新日期/Last Update: 2021-10-11