Semantic Segmentation for Fully Automated Macrofouling Analysis on Coatings after Field Exposure
Not on the map. Why?
Abstract (arXiv)
Biofouling is a major challenge for sustainable shipping, filter membranes, heat exchangers, and medical devices. The development of fouling-resistant coatings requires the evaluation of their effectiveness. Such an evaluation is usually based on the assessment of fouling progression after different exposure times to the target medium (e.g., salt water). The manual assessment of macrofouling requires expert knowledge about local fouling communities due to high variances in phenotypical appearance, has single-image sampling inaccuracies for certain species, and lacks spatial information. Here we present an approach for automatic image-based macrofouling analysis. We created a dataset with dense labels prepared from field panel images and propose a convolutional network (adapted U-Net) for the semantic segmentation of different macrofouling classes. The establishment of macrofouling localization allows for the generation of a successional model which enables the determination of direct surface attachment and in-depth epibiotic studies.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 77 references that are in the map. First posted on arXiv 21 Nov 2022.
Builds on
21 of 77 references.
References · 21
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
- 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
- 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
- 2017Squeeze-and-Excitation Networkscited by 4,763
- 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
- 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,261
- 2016Identity Mappings in Deep Residual Networkscited by 1,960
- 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
- 2015What's the Point: Semantic Segmentation with Point Supervisioncited by 297
9 more
- 2016Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentationcited by 198
- 2017Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentationcited by 145
- 2016The Importance of Skip Connections in Biomedical Image Segmentationcited by 126
- 2016The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentationcited by 122
- 2020Reinforced active learning for image segmentationcited by 56
- 2017Cost-Effective Active Learning for Melanoma Segmentationcited by 40
- 2015Introducing Geometry in Active Learning for Image Segmentationcited by 12
- 2017The Ciona17 Dataset for Semantic Segmentation of Invasive Species in a Marine Aquaculture Environmentcited by 4
- 2020Automating the assessment of biofouling in images using expert agreement as a gold standardcited by 3
Built on by
Citing papers per year: 2025: 1.
1 paper.