关键词: 目标检测, 特征融合, 注意力机制, 深度学习

Abstract:

Single Shot Multibox Detector（SSD） takes several feature layers for object detection, but each layer is used independently. This structure may ignore context information and is not conducive to optimize the detection accuracy of small objects. In order to improve the performance of SSD, a Feature Information Enhancement Based Single Shot Multibox Detector（FESSD） is proposed, and the core is a feature enhanced module. Firstly, a feature fusion module is proposed to fuse and refine the different feature layers. Then, a squeeze and excitation block is adopted to acquire the importance of each channel adaptively, so as to enhance the useful information and suppress the useless information. Finally, the simulation results show that, compared with the conventional SSD algorithm, the FESSD algorithm can improve the accuracy of object detection effectively.

Key words: object detection, feature fusion, attention mechanism, deep learning