关键词: 深度学习, SSD（Single Shot Multibox Detector）, 目标检测, 神经网络

Abstract:

Through the research and analysis of the original SSD（Single Shot Multibox Detector） model, the problem of its weak ability to detect small targets is solved. An improved model based on dense module and feature fusion is proposed. Based on Inception-ResNet-V2 and DenseNet, this model absorbs the research ideas of sparse connection in inception module and dense connection in dense network, and fuses the two methods together to propose the feature extraction structure of Inception-Dense. In the part of multi-scale detection, the feature fusion module of feature pyramid is used for reference and improved to strengthen the detection ability of small and medium-sized targets. The mapping mechanism of the default box is also reset according to the characteristics of the improved model and experimental data set. The results show that the average test accuracy（mAP） of this method on the Kitti dataset is 83.8%; the recognition rate is about 11 percentage points higher than the 72.8% of the original SSD model. FPS also has a nearly 38% improvement, from 39 to 54.

Key words: deep learning, SSD（Single Shot Multibox Detector）, target detection, neural network