改进丰富卷积特征算法的液滴边缘检测模型

王慧; 曹召良; 王军

doi:10.37188/CO.2024-0019

改进丰富卷积特征算法的液滴边缘检测模型

doi: 10.37188/CO.2024-0019

cstr: 32171.14.CO.2024-0019

王慧^1,,
曹召良^1,,
王军^2, ,

1.
苏州科技大学物理科学与技术学院, 江苏苏州 215009
2.
苏州科技大学电子与信息工程学院, 江苏苏州 215009

基金项目: “十四五”江苏省重点学科资助（No. 2021135）；中国航天科技集团公司第八研究院产学研合作基金资助（No. SAST2020-025）

详细信息

作者简介:
王军（1979—），男，江苏睢宁人，博士，副教授，2005年于中国科学院长春光学精密机械与物理研究所获得博士学位，主要从事光电测控技术、图像信息处理技术以及物联网工程应用方面的研究。E-mail：wjyhl@126.com

中图分类号: TP394.4;TH691.9
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- 被引次数: 0
出版历程
- 收稿日期: 2024-01-18
- 修回日期: 2024-01-30
- 录用日期: 2024-03-15
- 网络出版日期: 2024-05-10

Improved droplet edge detection model based on RCF algorithm

WANG Hui^1
,,
CAO Zhao-liang^1
,,
WANG Jun^{2
, ,}

1.
School of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou 215009, China
2.
School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China

Funds: Supported by Jiangsu Key Disciplines of the Fourteenth Five-Year Plan (No. 2021135); Industry-University-Institute Cooperation Foundation of the Eighth Research Institute of China Aerospace Science and Technology Corporation (No. SAST2020-025)

More Information

Corresponding author: wjyhl@126.com

摘要

摘要:
液滴图像边缘的高精度提取是测量水接触角较为关键的一环，针对常规边缘提取方法噪声鲁棒性差、边缘提取不完整、精度低的问题，本文提出了一种改进丰富卷积特征（RCF）的液滴边缘检测模型。首先，在深度特征提取阶段引入特征融合模块，使用多个特征让模型更加鲁棒，减少过拟合的风险；其次，设计多感受野模块代替RCF后边的contact层，通过多个感受野来提取更多的语义信息，使边缘细节更加丰富；然后，在模型每一层之前引入高效通道注意力机制，增强模型对图像中重要特征的关注程度；最后，设计并引入MaxBlurPool下采样技术，减少计算量和参数量，提高平移不变性。在自制液滴数据集上的实验结果表明，本文模型的固定轮廓阈值（ODS）提高到0.816、单图像最佳阈值（OIS）提高到0.829、检测准确率高达90.17%，相较原模型提高了1.85个百分点，能够准确检测液滴边缘特征。
- 深度学习 /
- 边缘检测 /
- 水接触角 /
- 特征融合 /
- 曲线拟合
Abstract:
Accurate droplet edge extraction is crucial for measuring water contact angle. To address issues like poor noise robustness, incomplete edge extraction, and low precision in conventional methods, we propose an improved model for droplet edge detection based on Richer Convolutional Feature (RCF) algorithm. Firstly, a feature fusion module is introduced in the deep feature extraction stage to enhance model robustness and reduce overfitting risks. Secondly, a multi-receptive field module replaces the contact layer after RCF to extract more semantic information and enrich edge details. Thirdly, an efficient channel attention mechanism is introduced before each layer of the models to enhance focus on important features of the image. Lastly, the MaxBlurPool downsampling technique is designed and incorporated to reduce computation and parameter requirements while improving translation invariance. Experimental results on a self-made droplet dataset demonstrate that the proposed model achieves an ODS value of 0.816, an OIS value of 0.829, and a detection accuracy of up to 90.17%, which is an improvement of 1.85 percentage points compared to the original model. It can improve accuracy in droplet edge features detections.
- deep learning /
- edge detection /
- water contact angle /
- feature fusion /
- curve fitting

HTML全文

图 1 RCF模型结构

Figure 1. RCF model structure

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图 2 改进的RCF模型结构

Figure 2. Improved RCF model structure

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图 3 特征融合

Figure 3. Feature fusion

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图 4 多感受野模块

Figure 4. Multi-receptive field module

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图 5 高效注意力机制模块

Figure 5. Efficient attention mechanism module

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图 6 抗锯齿的最大池化

Figure 6. Anti-aliasing for maximum pooling

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图 7 一维抗锯齿操作

Figure 7. One-dimensional anti-aliasing operation

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图 8 数据样本

Figure 8. Data samples

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图 9 传统RCF损失值与迭代次数的关系

Figure 9. Relationship between the loss and the number of iterations in the conventional RCF algorithm

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图 10 改进RCF损失值与迭代次数的关系

Figure 10. Relationship between the loss and the number of iterations in the improved RCF algorithm

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图 11 较小角度液滴边缘检测结果

Figure 11. Edge detection results for small-angle liquid droplet

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图 12 较大角度液滴边缘检测结果

Figure 12. Edge detection results for large-angle liquid droplet

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表 1 改进模型各阶段性能

Table 1. Performance of the improved model at each stage

Each stage	Precision/%	Recall/%	F-measure/%
Stage1	72.26	73.38	72.82
Stage2	78.31	81.69	79.96
Stage3	85.65	87.41	86.52
Stage4	88.92	89.15	89.03
Mixed Output	90.17	89.64	89.90

下载: 导出CSV

表 2 本文模型与其他算法结果比较

Table 2. Results comparison of the proposed model and other relevant algorithms

Algorithm	Precision/%	ODS	OIS	OR/%	Time/s
Canny	81.02	0.717	0.642	11.25	0.015
HED	87.49	0.732	0.735	7.36	0.025
RCF	88.32	0.783	0.792	6.74	0.028
Improved-RCF	90.17	0.816	0.829	5.10	0.041

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表 3 消融实验结果对比

Table 3. Comparison of ablation experimental results

Module	ODS	OIS
BasicRCF	0.783	0.792
BasicRCF+FM	0.795	0.812
BasicRCF+FM+DC	0.809	0.817
BasicRCF+FM+DC+EN	0.811	0.823
BasicRCF+FM+DC+EN+MP	0.816	0.829

下载: 导出CSV

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