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- W4287687958 abstract "The performance of a trained object detection neural network depends a lot on the image quality. Generally, images are pre-processed before feeding them into the neural network and domain knowledge about the image dataset is used to choose the pre-processing techniques. In this paper, we introduce an algorithm called ObjectRL to choose the amount of a particular pre-processing to be applied to improve the object detection performances of pre-trained networks. The main motivation for ObjectRL is that an image which looks good to a human eye may not necessarily be the optimal one for a pre-trained object detector to detect objects." @default.
- W4287687958 created "2022-07-26" @default.
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- W4287687958 date "2020-08-18" @default.
- W4287687958 modified "2023-09-29" @default.
- W4287687958 title "Reinforcement Learning for Improving Object Detection" @default.
- W4287687958 doi "https://doi.org/10.48550/arxiv.2008.08005" @default.
- W4287687958 hasPublicationYear "2020" @default.
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