Analyzing Condensation on Patterned Surfaces via Deep Learning
Authors: Tae E. Cooper, Michael Gee, Sheila Whitman, Jemma Dengel, Anthony Katselas, Duygu Dengiz, Frank Keutsch, Joanna Aizenberg
Organizations: School of Electrical, Computing, and Software Engineering, University of Arizona, Tucson, AZ. · John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA. · Department of Mathematics, University of Arizona, Tucson, AZ. · Department of Biomedical Engineering, University of Arizona, Tucson, AZ. · Department of Chemistry, University of Sydney, Sydney, NSW, Australia. · Department of Chemistry, Harvard University, Cambridge, MA.
Dropwise condensation is key to power and water systems. State-of-the-art condensation surfaces are often inhomogeneous, either patterned by design or random defects. Inhomogeneous surface features dictate the rates of each stage of dropwise condensation-nucleation, growth, coalescence, and shedding. Characterizing these rates requires the precise detection of thousands of droplets. Yet, existing segmentation methods are unable to detect droplets on inhomogeneous surfaces. We developed TileSAM3, a universal droplet segmentation tool built on the Segment Anything Model 3 (SAM3). Hierarchical tiling with per-tile inference followed by post-processing extended SAM3's detection cap and raised the effective resolution of small droplets. We created two datasets: High Quality Droplet Dataset (N=365,826; HQDD) with homogeneous surfaces and Low Quality Droplet Dataset (N=63,577; LQDD) with patterned, defect-laden surfaces under adverse lighting. TileSAM3 closely reproduced thresholding annotations on HQDD, surpassed it over sixfold on LQDD (Average Precision 0.57 versus 0.088), and outperformed other deep learning models on all metrics evaluated. Combined with KD-tree tracking, TileSAM3 identified localized nucleation and coalescence in narrow active bands on a stiffness-patterned surface. TileSAM3 enables analysis of understudied inhomogeneous substrates with poor lighting and videography which facilitates faster, lower cost research of condensation materials.
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Figure 1: TileSAM3 Pipeline. a) Images were split into N hierarchical layers of tiling. b) SAM3 inference was run once per tile (prompt = “droplet”). Droplets that were missed in one tile may have been detected in another tile over the same region in a different layer. c) Post-processing repaired masks by removing disconnected components and filling holes in masks. d) Duplicate masks between tiles and small masks fully contained within larger masks (shown in red) were removed. e) All masks were combined, which yielded a high-quality detection of droplets in the image.
Figure 2: Overview of the datasets and comparison of fine-tuned TileSAM3 with state-of-the-art and variations. a) High Quality Droplet Dataset (HQDD; N=365,826 annotations), an instance segmentation dataset for droplets on homogeneous surfaces under high quality imaging. On the lower right section, annotations (each identified drop is labeled by a distinct color) generated by binary thresholding are shown. b) Low Quality Droplet Dataset (LQDD; N=63,577 annotations), an instance segmentation dataset for droplets on inhomogeneous surfaces under low-quality imaging. On the lower right section, manually created ground truths by a combination of AI and human annotators are shown. c) Visual comparison of the predictions of binary thresholding (state-of-the-art; SOTA), Segment Anything Model 3 (SAM3) without fine-tuning (zero-shot), SAM3 fine-tuned on HQDD, and our TileSAM3 on an image from HQDD. SAM3 models were all prompted with “droplet.” d) Visual comparison with enlarged crops of the predictions of binary thresholding (SOTA), SAM3 without fine-tuning (zero-shot), SAM3 fine-tuned on LQDD, and our TileSAM3 on an image from LQDD. True positive detections are shown in green; false negative detections are shown in purple; false positive detections are shown in red. e) Distributions of droplet count as a function of droplet volume from predictions on HQDD by models averaged across five-fold cross-validation. Thresholding was taken as the ground truth. f) Distributions of droplet count as a function of droplet volume from predictions on LQDD by models averaged across five-fold cross-validation. Droplet volumes were obtained by assuming droplets were spherical caps with a material dependent contact angle.
Figure 3: Qualitative and quantitative benchmark on LQDD. Segmentation predictions of four models: a) U-Net, b) Mask R-CNN, c) SAM3, and d) TileSAM3 on three challenging LQDD images: (i) vignette lighting that illuminates only the central region, (ii) dark vertical lines that interrupt droplet boundaries, and (iii) small surface imperfections and lighting variations that reduce local droplet contrast. The top row shows the original images. True-positive detections are shown in green, false-negative detections in purple, and false-positive detections in red. The right-hand column reports three metrics per model–Symmetric Best Dice (SBD), Average Precision for small droplets ( APsmall ), and overall Average Precision (AP)–averaged over five-fold cross-validation on LQDD.
Figure 4: Droplet Tracking on a Patterned Surface. a) A KD-tree algorithm matched TileSAM3 predictions on frames of droplet formation in 10-second intervals. Tracking was visualized by overlaying matching droplets between frames. When droplets merged, the color of the resulting droplet was randomly chosen from the two parent droplets. b) Heatmap and row-averaged heat bar of percent occupancy over 620s of droplet formation. c) Quiver plot of local average droplet velocity across the surface. A clear pattern emerged as droplets formed in thin bands and stayed in an unstable equilibrium before migrating to larger stable regions. d) Droplet formation rate across horizontal bands of the video. The greatest amount of droplet formation occurred in the regions between stable bands. e) Coalescence rate across horizontal bands of the video. The rate of coalescence was greatest in the same regions as droplet formation–between stable bands.
Department of Earth Science and Engineering, Imperial College London, London SW7 2AZ, United Kingdom · College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China