论文标题
用于高精度对象检测的模块化网络
Modular network for high accuracy object detection
论文作者
论文摘要
我们提出了一种新型的模块化对象检测卷积神经网络，可显着提高对象检测的准确性。该网络由分层结构中的两个阶段组成。第一阶段是一个检测通用类的网络。第二阶段由单独的网络组成，以完善每个通用类对象的分类和本地化。与最先进的对象检测网络相比，模块化网络中的分类误差大约提高了3-5倍，从12％到2.5％-4.5％。该网络易于实现，并具有0.94的地图。该网络体系结构可以是提高最广泛状态对象检测网络和其他类型的深度学习网络的准确性的平台。我们表明，随着后来训练以检测到的类的类数，通过转移学习初始化的深度学习网络变得更加准确。
We present a novel modular object detection convolutional neural network that significantly improves the accuracy of object detection. The network consists of two stages in a hierarchical structure. The first stage is a network that detects general classes. The second stage consists of separate networks to refine the classification and localization of each of the general classes objects. Compared to a state of the art object detection networks the classification error in the modular network is improved by approximately 3-5 times, from 12% to 2.5 %-4.5%. This network is easy to implement and has a 0.94 mAP. The network architecture can be a platform to improve the accuracy of widespread state of the art object detection networks and other kinds of deep learning networks. We show that a deep learning network initialized by transfer learning becomes more accurate as the number of classes it later trained to detect becomes smaller.
