Computer Vision-Based On-Site Estimation of Contact Angle From 3-D Reconstruction of Droplets

Current methods to measure the contact angle require orthogonal imaging of the droplet and substrate. We have developed a novel computer vision-based technique to reconstruct the surface of the 3-D transparent microdroplet from nonorthogonal images and determined the contact angle using custom-made equipment comprising a smartphone camera and macro lens. After estimating the intrinsic and extrinsic camera parameters using a printed pattern, the EfficientNet-B4 model of U-Net convolutional neural network (CNN) architecture was used to extract silhouettes of droplets from images using semantic segmentation. Finally, the shape-from-silhouette method was employed involving a space carving algorithm to estimate the visual hull containing the droplet volume. Comparison with measurements from a state-of-the-art goniometer of static and dynamic contact angles on various substrates using a standard goniometer revealed an average error of 4%. Our method, using nonorthogonal images, was found to be successful for the on-site measurement of static and dynamic contact angles, as well as 3-D reconstruction of the transparent droplets.