Hierarchical classification of very small objects: Application to the detection of arthropod species
| Autor: | Paul Tresson, William Puech, Philippe Tixier, Dominique Carval |
|---|---|
| Přispěvatelé: | Image & Interaction (ICAR), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), ANR-16-CONV-0004,DIGITAG,Institut Convergences en Agriculture Numérique(2016) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
02 engineering and technology robustness computer.software_genre taxonomy Very small object detection 0202 electrical engineering electronic engineering information engineering General Materials Science apprentissage machine biodiversity Traitement des données 0303 health sciences Biological data General Engineering Classification [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR] 020201 artificial intelligence & image processing Electrical engineering. Electronics. Nuclear engineering Biodiversité Transfer of learning Analyse d'image Similarity (geometry) General Computer Science Arthropoda Traitement d'images Analyse de réseau Machine learning Image (mathematics) 03 medical and health sciences 030304 developmental biology business.industry Deep learning deep learning L60 - Taxonomie et géographie animales Object (computer science) Object detection TK1-9971 Task analysis Artificial intelligence U30 - Méthodes de recherche business computer |
| Zdroj: | IEEE Access IEEE Access, Vol 9, Pp 63925-63932 (2021) IEEE Access, IEEE, 2021, 9, pp.63925-63932. ⟨10.1109/ACCESS.2021.3075293⟩ |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/ACCESS.2021.3075293⟩ |
| Popis: | International audience; Automated image analysis and deep learning tools such as object detection models are being used increasingly by biologists. However, biological datasets often have constraints that are challenging for the use of deep learning. Classes are often imbalanced, similar, or too few for robust learning. In this paper we present a robust method relying on hierarchical classification to perform very small object detection. We illustrate our results on a custom dataset featuring 22 classes of arthropods used to study biodiversity. This dataset shows several constraints that are frequent when using deep learning on biological data with a high class imbalance, some classes learned on only a few training examples and a high similarity between classes. We propose to first perform detection at a super-class level, before performing a detailed classification at a class level. We compare the obtained results with our proposed method to a global detector, trained without hierarchical classification. Our method succeeds in obtaining a mAP of 75 %, while the global detector only achieves a mAP of 48 %. Moreover, our method shows high precision even on classes with the less train examples. Confusions between classes with our method are fewer and are of a lesser impact. We are able achieve a more robust object classification with the use of our proposed method. This method can also enable better control on the model's output which can be particularly valuable when handling ecological, biological or medical data for example. |
| Databáze: | OpenAIRE |
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