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Title: Pedestrian detection algorithm based on improved SSD

Authors: Dawei Liu; Shang Gao; Wanda Chi; Di Fan

Addresses: College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China ' College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China ' College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China ' College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China

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.

Keywords: level feature fusion; decode-encoder; pedestrian detection; semantic features; SSD; single shot multibox detector; depthwise separable convolution.

DOI: 10.1504/IJCAT.2021.113643

International Journal of Computer Applications in Technology, 2021 Vol.65 No.1, pp.25 - 35

Received: 25 May 2020
Accepted: 10 Jul 2020

Published online: 06 Mar 2021 *

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