1 research outputs found
Reinforcement Learning for Improving Object Detection
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.Comment: 14 pages, 6 figures, 4 tables. Accepted in the RLQ-TOD workshop at
ECCV 202