Reinforcement Learning for Improving Object Detection

• URL: http://arxiv.org/abs/2008.08005v1
• Date: Tue, 18 Aug 2020 16:20:04 GMT
• Title: Reinforcement Learning for Improving Object Detection
• Authors: Siddharth Nayak and Balaraman Ravindran
• Abstract summary: 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.
• Score: 22.06211725256875
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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