一种可变锚框候选区域网络的目标检测方法

目标检测作为计算机视觉领域的热点问题，目前基于深度学习的目标检测方法可以分为2类:两步检测和一步检测，前者有着较高准确性，后者有着较好速度，但是为提高检测的性能两者都引入了锚机制。为提高目标检测系统的性能，基于深度卷积神经网络的两步检测算法引入了注意力引导（AG）模块，通过对候选区域网络（RPN）的锚机制进行引导，使得对于预选锚框形状的选择更具有多样性；同时针对传统的后处理方式非极大值抑制（NMS）算法存在的误检和漏检的问题，提出了一种置信度因子的NMS（Cf-NMS）算法，对于模型的整体性能有着很大的贡献。实验结果说明，所提方法虽然在速度性能上有略微的下降，但是无论是在RPN变体还是现有的先进算法在准确性方面都有提升。 

Target detection method for region proposal network with variable anchor box

Object detection is a hot topic in the field of computer vision. At present, object detection methods based on deep learning can be divided into two categories: two-stage detection and one-stage detection. The former has higher accuracy, while the latter has better speed. In order to improve the performance of detection, anchor mechanism is introduced in both categories. In this paper, Attention Guidance (AG) module is introduced in the two-stage detection method based on the deep convolutional neural network, which guides the anchor mechanism of Region Proposal Network (RPN), making the selection of preselected box shape more diversified. At the same time, to solve the problem of false detection and missed detection in the traditional post-processing Non-Maximum Suppression (NMS) algorithm, a Confidence factor NMS (Cf-NMS) method is proposed, which makes a great contribution to the overall performance of the model. Experiment results showed that, although it has a slight decrease in speed performance, the proposed method has an improvement in accuracy in both the RPN variant and the existing advanced method.