Pedestrian detection algorithm based on improved SSD

Abstract

The algorithm proposed in this paper introduces the deeply separable fusion hierarchical feature model into the backbone network of Single Shot MultiBox Detector (SSD) algorithm, which reduces the complexity of the model by using depthwise Separable Convolution and uses hierarchical structure fusion to enhance features. We add decode-encoder structure between the backbone network and the additional feature layer to propagate the high-level semantic feature downward through decoding, and then integrate the feature with the shallow local detail feature. At the same time, the encoder is used to transfer more advanced semantic features and output the fused shallow features for pedestrian detection, which improves the classification and detection ability of the model. The miss rate of the improved algorithm in this paper is as low as 9.68% on the INRIA data set.