CFNet: Head detection network based on multi‐layer feature fusion and attention mechanism

Author:

Han Jing1,Wang Xiaoying1,Wang Xichang1,Lv Xueqiang1

Affiliation:

1. Beijing Key Laboratory of Internet Culture Digital Dissemination Beijing Information Science and Technology University Beijing China

Abstract

AbstractRecently, head detection has been widely used in target detection, which has a great application value for improving security prevention and control in public places, as well as enhancing target tracking and identification in national defense, criminal investigation, and other fields. However, detecting small targets accurately at long distances is very difficult, and current methods often lack optimization of multi‐resolution features. Therefore, the authors propose a one‐stage detection network CFNet (cross‐layer feature fusion and fusion weight attention network), in which a fusion weight attention mechanism module (FWAM) is proposed to give different weights to the fused features in order to distinguish the importance of different features. The module increases the weights of features that contain strong information so that the fused features are focused on feature points that are beneficial for optimal head detection. Meanwhile, a cross‐layer feature fusion module is proposed to fuse information from different resolution feature maps to compensate for the decrease in detection accuracy caused by the omission of information features at low resolution, and a connection network for contextual information fusion is constructed, while weight parameter value settings are introduced to optimize the detection effect after fusion of different resolution features. In order to better reflect the effectiveness of the network, the experiments are performed on the SCUT‐HEAD PartA dataset and the Brainwash dataset; the results show that the network the authors proposed is better than the existing comparison methods, which proves the robustness and effectiveness of the network.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Beijing Municipality

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference19 articles.

1. Intensive crowd safety monitoring system based on deep learning;Wang X.Y.;Int. Things Technol,2019

2. Example-based object detection in images by components

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