Research on a Pedestrian Detection Algorithm Based on Improved SSD Network

Abstract

When the SSD network uses an independent feature layer to detect objects, there is no connection between the layers, which leads to the problem of insufficient expression of contextual features. This paper proposes a pedestrian detection algorithm based on improved SSD network. The algorithm uses cross-layer feature adaptive fusion, and combines residual channel attention modules with different convolution rates of holes. While increasing the receptive field, this algorithm enhances important features and weakens unimportant features. Make the extracted features more directional, thereby improving the accuracy of pedestrian detection. Experiment on the improved network and algorithm on the INRIA pedestrian detection dataset, and the mixed pedestrian dataset extracted from the COCO dataset and the Crowd human dataset. The experimental results show that the average precision of pedestrian detection in the two datasets is improved by 1.7% and 4.0% respectively compared with the original network.