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Cited by 15Advanced Neural Network ApplicationsHuman Pose and Action RecognitionVideo Surveillance and Tracking Methods

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ABYOLOv4: improved YOLOv4 human object detection based on enhanced multi-scale feature fusion

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Li, Rui, Zeng, Xin, Yang, Shiqiang, Li, Qi, Yan, An, Li, Dexin·2024·EURASIP Journal on Advances in Signal Processing
DOI 10.1186/s13634-023-01105-z
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Abstract

The purpose of human object detection is to obtain the number of people and their position in images, which is one of the core problems in the field of machine vision. However, the high missing detection rate from small- and medium-sized human bodies due to the large variety of human scale in human object detection tasks still influences the performance of human object detection. To solve the above problem, this paper proposed an improved ASPP_BiFPN_YOLOv4 (ABYOLOv4) method to detect human object detection. In detail, Atrous Spatial Pyramid Pooling (ASPP) module was used to replace the original Spatial Pyramid Pooling module to increase the receptive field level of the network and improve the perception ability of multi-scale targets. Then, the original Path Aggregation Network (PANet) multi-scale fusion module was replaced by the self-built bi-layer bidirectional feature pyramid network (Bi-FPN). Meanwhile, a new feature was imported into the proposed model to reuse the mid- and low-level features, which could enhance the ability of the network to express the characteristics of small- and medium-sized targets. Finally, the standard convolution in Bi-FPN was replaced by depth-separable convolution to make the network achieve the balance of accuracy and the number of parameters. To identify the performance of the proposed ABYOLOv4 model, the human object detection experiment is carried out by using the public data set of VOC2007 and VOC2012, the improved YOLOv4 algorithm is 0.5% higher than the original AP algorithm, and the weight file size of the model is reduced by 45.3 M. The experimental results demonstrated that the proposed ABYOLOv4 network has higher accuracy and lower computational cost for human target detection.

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Research works printed but not linked: 18

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Metadata

Authors
Li, R., Zeng, X., Yang, S., Li, Q., Yan, A., & Li, D.
Affiliations
Xi'an University of Technology (CN)
Corresponding author
Yang, Shiqiang
ORCID
Venue
EURASIP Journal on Advances in Signal Processing
Publisher
Springer Science and Business Media LLC
Published
2024-01-04
Volume
2024
Issue
1
ISSN
  • 1110-8657 (ISSN-L)
  • 1687-6180
DOI
10.1186/s13634-023-01105-z
OpenAlex
W4390580017
Cited by
15
References
7
Citations by year
2026: 3 · 2025: 7 · 2024: 5
FWCI
2.10
Type
journal article
Language
en
Open access
Licence
Locations
  • EURASIP Journal on Advances in Signal Processing (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Shaanxi Provincial Department of Education 2022 General Special Research Program Projects (22JK0471)
  • China Postdoctoral Foundation Project (2022MD723841)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
Keywords
human object detection, YOLOv4, multi-scale feature fusion, BiFPN, atrous spatial pyramid pooling, depthwise separable convolution, model compression, VOC2007

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