Robust detection of marine life with label-free image feature learning and probability calibration Article Swipe
Tobias Schanz
,
Klas Ove Möller
,
Saskia Rühl
,
David S. Greenberg
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/2632-2153/ace417
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/2632-2153/ace417
Advances in in situ marine life imaging have significantly increased the size and quality of available datasets, but automatic image analysis has not kept pace. Machine learning has shown promise for image processing, but its effectiveness is limited by several open challenges: the requirement for large expert-labeled training datasets, disagreement among experts, under-representation of various species and unreliable or overconfident predictions. To overcome these obstacles for automated underwater imaging, we combine and test recent developments in deep classifier networks and self-supervised feature learning. We use unlabeled images for pretraining deep neural networks to extract task-relevant image features, allowing learning algorithms to cope with scarcity in expert labels, and carefully evaluate performance in subsequent label-based tasks. Performance on rare classes is improved by applying data rebalancing together with a Bayesian correction to avoid biasing inferred in situ class frequencies. A divergence-based loss allows training on multiple, conflicting labels for the same image, leading to better estimates of uncertainty which we quantify with a novel accuracy measure. Together, these techniques can reduce the required label counts ∼100-fold while maintaining the accuracy of standard supervised training, shorten training time, cope with expert disagreement and reduce overconfidence.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/2632-2153/ace417
- https://iopscience.iop.org/article/10.1088/2632-2153/ace417/pdf
- OA Status
- gold
- Cited By
- 4
- References
- 78
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4383105644Canonical identifier for this work in OpenAlex
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https://doi.org/10.1088/2632-2153/ace417Digital Object Identifier
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Robust detection of marine life with label-free image feature learning and probability calibrationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-04Full publication date if available
- Authors
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Tobias Schanz, Klas Ove Möller, Saskia Rühl, David S. GreenbergList of authors in order
- Landing page
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https://doi.org/10.1088/2632-2153/ace417Publisher landing page
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https://iopscience.iop.org/article/10.1088/2632-2153/ace417/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://iopscience.iop.org/article/10.1088/2632-2153/ace417/pdfDirect OA link when available
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Computer science, Artificial intelligence, Machine learning, Pattern recognition (psychology), Feature (linguistics), Classifier (UML), Convolutional neural network, Artificial neural network, Feature learning, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 3, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3140854437, https://openalex.org/W6795754764, https://openalex.org/W6757407635, https://openalex.org/W6674878840, https://openalex.org/W6794655914, https://openalex.org/W6774314701, https://openalex.org/W6779977557, https://openalex.org/W6774670964, https://openalex.org/W6652419235, https://openalex.org/W6717613756, https://openalex.org/W6796931752, https://openalex.org/W2018580505, https://openalex.org/W6846642385, https://openalex.org/W2346813889, https://openalex.org/W6912515347, https://openalex.org/W6838117315, https://openalex.org/W2049489886, https://openalex.org/W2962949934, https://openalex.org/W3109526273, https://openalex.org/W2118978333, https://openalex.org/W3035524453, https://openalex.org/W6687483927, https://openalex.org/W2084977633, https://openalex.org/W2993464084, https://openalex.org/W6631190155, https://openalex.org/W6838646311, https://openalex.org/W4294242935, https://openalex.org/W3212185757, https://openalex.org/W4282000490, https://openalex.org/W3208685304, https://openalex.org/W1999803211, https://openalex.org/W6639102338, https://openalex.org/W2917262027, https://openalex.org/W2984313420, https://openalex.org/W2896018215, https://openalex.org/W2150831270, https://openalex.org/W2142283269, https://openalex.org/W6766978945, https://openalex.org/W6804002281, https://openalex.org/W4281762078, https://openalex.org/W2117539524, https://openalex.org/W4295122555, https://openalex.org/W6730323794, https://openalex.org/W1530098540, https://openalex.org/W2962843773, https://openalex.org/W3119770130, https://openalex.org/W6743795293, https://openalex.org/W3150635270, https://openalex.org/W3192495122, https://openalex.org/W6845699158, https://openalex.org/W6802900763, https://openalex.org/W6744513255, https://openalex.org/W2964137095, https://openalex.org/W2781072509, https://openalex.org/W2034841618, https://openalex.org/W4321022081, https://openalex.org/W3100859887, https://openalex.org/W2427657585, https://openalex.org/W2754549959, https://openalex.org/W2757910899, https://openalex.org/W4303648950, https://openalex.org/W4286695273, https://openalex.org/W4295312788, https://openalex.org/W2008381331, https://openalex.org/W3171087525, https://openalex.org/W4281636746, https://openalex.org/W2194775991, https://openalex.org/W4310638179, https://openalex.org/W1861492603, https://openalex.org/W1522301498, https://openalex.org/W2555897561, https://openalex.org/W3205809313, https://openalex.org/W3009561768, https://openalex.org/W3159481202, https://openalex.org/W3005680577, https://openalex.org/W4281621864, https://openalex.org/W2101156862, https://openalex.org/W2905050155 |
| referenced_works_count | 78 |
| abstract_inverted_index.A | 139 |
| abstract_inverted_index.a | 128, 162 |
| abstract_inverted_index.To | 62 |
| abstract_inverted_index.We | 84 |
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| abstract_inverted_index.in | 2, 3, 76, 105, 112, 135 |
| abstract_inverted_index.is | 37, 120 |
| abstract_inverted_index.of | 15, 54, 156, 180 |
| abstract_inverted_index.on | 117, 144 |
| abstract_inverted_index.or | 59 |
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| abstract_inverted_index.we | 70, 159 |
| abstract_inverted_index.and | 13, 57, 72, 80, 108, 191 |
| abstract_inverted_index.but | 18, 34 |
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| abstract_inverted_index.the | 11, 43, 149, 171, 178 |
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| abstract_inverted_index.deep | 77, 90 |
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| abstract_inverted_index.inferred | 134 |
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| abstract_inverted_index.overcome | 63 |
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| abstract_inverted_index.standard | 181 |
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| corresponding_author_ids | https://openalex.org/A5053715725 |
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| corresponding_institution_ids | https://openalex.org/I4210139239 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/14 |
| sustainable_development_goals[0].score | 0.8199999928474426 |
| sustainable_development_goals[0].display_name | Life below water |
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| citation_normalized_percentile.is_in_top_10_percent | False |