中国光学（中英文）2024，Vol.17Issue(4)：886-895,10.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
Improved droplet edge detection model based on RCF algorithm
摘要
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 pro-pose 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 robust-ness 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 im-age.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.关键词
深度学习/边缘检测/水接触角/特征融合/曲线拟合Key words
deep learning/edge detection/water contact angle/feature fusion/curve fitting分类
信息技术与安全科学引用本文复制引用
王慧,曹召良,王军..改进丰富卷积特征算法的液滴边缘检测模型[J].中国光学（中英文）,2024,17(4):886-895,10.基金项目
"十四五"江苏省重点学科资助(No.2021135) （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)Supported by Jiangsu Key Disciplines of the Fourteenth Five-Year Plan(No.2021135) （No.SAST2020-025）
Industry-University-In-stitute Cooperation Foundation of the Eighth Research Institute of China Aerospace Science and Technology Corporation(No.SAST2020-025) （No.SAST2020-025）
