论文标题
加强学习以改善对象检测
Reinforcement Learning for Improving Object Detection
论文作者
论文摘要
训练有素的对象检测神经网络的性能在很大程度上取决于图像质量。通常，在将图像馈入神经网络之前，对图像进行了预处理，并且有关图像数据集的域知识用于选择预处理技术。在本文中，我们介绍了一种称为Objectrl的算法，以选择用于改善预训练网络的对象检测性能的特定预处理的数量。 ObjectRL的主要动机是，看起来对人眼看起来不错的图像不一定是预训练的对象检测器检测对象的最佳图像。
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.
