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.