SSD Object Detection Algorithm with Effective Fusion of Attention and Multi-scale

Abstract

In order to solve the problems of weak effective information of feature map and high miss rate of difficult objects in the traditional single shot multibox detector (SSD) for multi-scale object detection, an improved SSD object detection algorithm is proposed. Firstly, a lightweight attention mechanism is introduced at the output of the network feature map. Through non-dimensionality reduction, local cross-channel interaction and adaptive core size selection, it can effectively highlight the key information in the feature map while maintaining the original amount of network computation. This module helps to enhance the difference between background information and object information, and can effectively improve the performance of the network without increasing the complexity of the network. Then, a new feature fusion module is designed to effectively fuse features of different scales. It can make the shallow feature layer not only contain rich detailed information, but also make full use of contextual semantic information. The multi-scale fusion module helps to enrich the feature map information and improve the detection performance of the network for difficult objects. The experimental results on the PASCAL VOC dataset show that the improved network has a detection accuracy of 79.6% on the PASCAL VOC2007 test set, which is increased by 2.4 percentage points than the original SSD algorithm, and increased by 4.7 percentage points on the occlusion target dataset. It is proven that the improved method has certain timeliness and robustness.

Keywords

• |object detection|deep learning|lightweight attention mechanism|multi-scale feature fusion