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WANG Hui, CAO Zhao-liang, WANG Jun. Improved droplet edge detection model based on RCF algorithm[J]. Chinese Optics. doi: 10.37188/CO.2024-0019
Citation: WANG Hui, CAO Zhao-liang, WANG Jun. Improved droplet edge detection model based on RCF algorithm[J]. Chinese Optics. doi: 10.37188/CO.2024-0019

Improved droplet edge detection model based on RCF algorithm

doi: 10.37188/CO.2024-0019
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)
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  • Corresponding author: wjyhl@126.com
  • Received Date: 18 Jan 2024
  • Accepted Date: 15 Mar 2024
  • Available Online: 10 May 2024
  • 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 RCF. 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 of the models layer 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.


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