结合多通道注意力机制的目标检测

Object Detection Combined with Multi-Channel Attention Mechanism

在计算机图像处理中,注意力机制能够有效帮助深度网络更好地关注相关目标区域。为提高目标检测性能,提出多通道注意力机制;上述方法由多分通道注意力机制和扩大感受野模块组成,其中多分通道注意力机制是对原始特征图进行可学习区域划分,对不同区域分别使用通道注意力机制;扩大感受野模块是在扩大感受野的同时对独立通道进行加权,并建立全局通道关系。在不同目标检测网络中验证所提方法,网络性能均有所提高。新方法优于传统的注意力机制,在增加微小计算成本的情况下有效提高目标检测的精度。

In computer image processing,attention mechanisms can effectively help CNNs better focus on relevant target regions.In order to improve the performance of object detection,this paper proposes a multi-channel attention mechanism,which is composed of a chunked-channel attention mechanism and an expanded receptive field module,in which the chunked-channel attention mechanism is to divide the learning regions of the original feature map,and use the channel attention mechanism for different regions;the expanded receptive field module weights the independ⁃ent channel while expanding the receptive field,and establishes the global channel relationship.This method has been verified in different object detection networks,and the network performance has been improved.This method is superior to the traditional attention mechanism,effectively improving the accuracy of target detection with less extra computation.