Mr. Annette Stahl Profile

Annette Stahl

at NTNU

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Proceedings Article | 30 June 2021 Paper

A combined informative and representative active learning approach for plankton taxa labeling

Martin Lund Haug, Aya Saad, Annette Stahl

Proceedings Volume 11878, 118781Q (2021) https://doi.org/10.1117/12.2601096

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With an ever-increasing amount of image data, the manual labeling process has become the bottleneck in many machine learning applications. Plankton taxa labeling is especially a challenge due to its complex nature, and the manual labeling effort places a large burden on the domain experts. The Active Learning (AL) paradigm is a promising research direction adopted in the literature to minimize the manual labeling effort exerted by domain experts. Many approaches for AL have been proposed over the recent years to improve the labeling task by supporting the construction of large datasets suitable to train machine learning models while minimizing human involvement in the process. Our empirical study suggests that many modern active learning methods fail to incorporate both the samples that represent the statistical pattern of the data and the samples in which the machine learning model is not confident about. Inspired by these limitations, we propose an algorithm that combines these two types of sampling in order to capture the data distribution of the whole feature space, prevent redundant sampling from correlated uncertainty queries and finetune the inter-class decision boundary. Our experiments show that the proposed method outperforms each of the methods separately. Further, it also proves to be efficient on both the CIFAR dataset and the more complex Kaggle plankton dataset.

Proceedings Article | 4 January 2021 Paper

An instance segmentation framework for in-situ plankton taxa assessment

Aya Saad, Sondre Bergrum, Annette Stahl

Proceedings Volume 11605, 1160511 (2021) https://doi.org/10.1117/12.2587693

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In this paper, we propose a deep learning instance segmentation framework for particle extraction of microscopic images that aims at calculating planktonic species distribution and concentration in-situ. The framework comprises three essential functional tasks on in-situ time-series images collected from an autonomous underwater vehicle: 1) manual labeling of the captured images, 2) object localization, segmentation, and identification, and 3) class distribution and planktonic organisms concentration calculation. Our proposed framework is based on the mask R-CNN architecture provided by the Detectron2 library developed by Facebook Artificial Intelligence Research (FAIR) for instance segmentation. Due to its modular design, we compare the performance of different networks by alternating the backbone sub-network in order to choose the most suitable architecture for the task of instance and semantic segmentation. We compile a custom annotated dataset from planktonic time-series images and train the different models over this dataset to perform the instance semantic segmentation. Evaluation results of the proposed framework, utilizing the best performing deep learning architecture along with the new annotated dataset, show better performance in terms of speed and accuracy of both in-situ segmentation and classification compared to traditional segmentation methods. In addition, we observe a significant improvement in the object classification quality when we train the model over our newly annotated dataset instead of training it over the dataset generated from the traditional methods. The inferred data from our novel instance segmentation framework, which provides the particle class distribution and concentration, can then be used to assist in constructing a dynamic probability density map of planktonic communities dispersion and abundance.

Proceedings Article | 31 January 2020 Paper

Classification of corrosion and coating damages on bridge constructions from images using convolutional neural networks

Egil Holm, Aksel Transeth, Ole Knudsen, Annette Stahl

Proceedings Volume 11433, 1143320 (2020) https://doi.org/10.1117/12.2557380

KEYWORDS: Corrosion, Image classification, Bridges, Inspection, Coating, Convolutional neural networks, Damage detection, MATLAB, Roads, Network architectures

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In this paper, we present a comparison of performance for different convolutional neural networks (CNN) for automatic classification of corrosion and coating damages on bridge constructions from images. Image recordings were taken during inspections. Through manual categorization and data augmentation, a total of 9300 images were collected and divided into five classes. Four different CNNs were trained using transfer learning in MATLAB. We have evaluated test performance through the metrics recall, precision, accuracy and F1 score. Test performance was also evaluated on damage detection accuracy, meaning how well the networks detect images that contain a damage. The convolutional neural network trained using VGG-16 had the overall best performance results, with average recall, precision, accuracy and F1 score being 95.45%, 95.61%, 97.74% and 95.53%, respectively. In the category of overall damage detection AlexNet performed best with 99.14% accuracy. The obtained results are promising, and make it possible to conclude that CNNs have a great potential in bridge inspections for automatic analysis of corrosion and coating damages.

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