基于注意力机制的单发多框检测器算法

单发多框检测器SSD是一种在简单、快速和准确性之间有着较好平衡的目标检测器算法。SSD网络结构中检测层单一的利用方式使得特征信息利用不充分，将导致小目标检测不够鲁棒。该文提出一种基于注意力机制的单发多框检测器算法ASSD。ASSD算法首先利用提出的双向特征融合模块进行特征信息融合以获取包含丰富细节和语义信息的特征层，然后利用提出的联合注意力单元进一步挖掘重点特征信息进而指导模型优化。最后，公共数据集上进行的一系列相关实验表明ASSD算法有效提高了传统SSD算法的检测精度，尤其适用于小目标检测。

Attention Based Single Shot Multibox Detector

Single Shot multibox Detector (SSD) is a object detection algorithm that provides the optimal trade-off among simplicity, speed and accuracy. The single use of detection layers in SSD network structure makes the feature information not fully utilized, which will lead to the small object detection are not robust enough. In this paper, an Attention based Single Shot multibox Detector (ASSD) is proposed. The ASSD algorithm first uses the proposed two-way feature fusion module to fuse the feature information to obtain the feature layer which containing rich details and semantic information. Then, the proposed joint attention unit is used to mine further the key feature information to guide the model optimization. Finally, a series of experiments on the common data set show that the ASSD algorithm effectively improves the detection accuracy of conventional SSD algorithm, especially for small object detection.