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- W4306406510 abstract "When training a neural network for object detection a great deal of effort is usually devoted to augment the training dataset. The rationale behind this process is that augmentation increases the generalization capability of the network. However, little attention has been paid to the application of image enhancement techniques as a pre-processing step of the training task. In this paper we show, in the context of fish detection in submarine images, that the application of classical color enhancement methods may improve significantly the performance of the well known Mask R-CNN object detector." @default.
- W4306406510 created "2022-10-17" @default.
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- W4306406510 date "2022-10-17" @default.
- W4306406510 modified "2023-10-18" @default.
- W4306406510 title "On the Importance of Color Pre-Processing for Object Detection in Submarine Images" @default.
- W4306406510 doi "https://doi.org/10.3233/faia220344" @default.
- W4306406510 hasPublicationYear "2022" @default.
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