TagLab: AI‐assisted annotation for the fast and accurate semantic segmentation of coral reef orthoimages
Pavoni, G.; Corsini, M.; Ponchio, F.; Muntoni, A.; Edwards, C.; Pedersen, N.; Sandin, S.; Cignoni, P. (2022). TagLab: AI‐assisted annotation for the fast and accurate semantic segmentation of coral reef orthoimages. J. Field Robot. 39(3): 246-262. https://dx.doi.org/10.1002/rob.22049
In: Journal of Field Robotics. Wiley: Hoboken. ISSN 1556-4959; e-ISSN 1556-4967, more
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| Author keywords |
artificial intelligence; computer vision |
| Authors | | Top |
- Pavoni, G.
- Corsini, M.
- Ponchio, F.
- Muntoni, A.
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- Edwards, C.
- Pedersen, N.
- Sandin, S.
- Cignoni, P.
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| Abstract |
Semantic segmentation is a widespread image analysis task; in some applications, it requires such high accuracy that it still has to be done manually, taking a long time. Deep learning-based approaches can significantly reduce such times, but current automated solutions may produce results below expert standards. We propose agLab, an interactive tool for the rapid labelling and analysis of orthoimages that speeds up semantic segmentation. TagLab follows a human-centered artificial intelligence approach that, by integrating multiple degrees of automation, empowers human capabilities. We evaluated TagLab's efficiency in annotation time and accuracy through a user study based on a highly challenging task: the semantic segmentation of coral communities in marine ecology. In the assisted labelling of corals, TagLab increased the annotation speed by approximately 90% for nonexpert annotators while preserving the labelling accuracy. Furthermore, human–machine interaction has improved the accuracy of fully automatic predictions by about 7% on average and by 14% when the model generalizes poorly. Considering the experience done through the user study, TagLab has been improved, and preliminary investigations suggest a further significant reduction in annotation times. |
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