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research-article

FM-YOLO Object Detection Algorithm

Authors:

Chuanzhong Pan,

Ruixian ZhangAuthors Info & Claims

DSDE '22: Proceedings of the 2022 5th International Conference on Data Storage and Data Engineering

Pages 102 - 107

https://doi.org/10.1145/3528114.3528131

Published: 24 June 2022 Publication History

Abstract

Aiming at the problems of YOLOv4 with many parameters and low detection accuracy, a lightweight FM-YOLO (Fused Mobile-You Only Look Once) object detection algorithm is proposed. The algorithm improves the convolution layer of the deep network for feature extraction in two aspects. First, MBConv is used to expand the width of the convolution operation and reduce the amount of model parameters. Secondly, Fused-MBConv in the shallow network solves the problem that the depthwise separable convolution cannot fully utilize GPU acceleration in the shallow network, resulting in slower training speed. The activation function is changed from Mish to SiLU, and dropout is added to avoid network training over-fitting. From the test results of the VOC2007 data set, the parameter of the YOLOv4 algorithm is reduced by 1.4M compared with YOLOv4, and the mAP is increased by 1.45%. This algorithm improves the accuracy of detection while slightly reducing the amount of model parameters.

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Cited By

Yin YKou TOchieng NBao X(2023)PASFLN: Positional Association and Semantic Fusion Learning Network for Traffic Object Detection2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC57777.2023.10422508(329-334)Online publication date: 24-Sep-2023
https://doi.org/10.1109/ITSC57777.2023.10422508

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DSDE '22: Proceedings of the 2022 5th International Conference on Data Storage and Data Engineering

February 2022

124 pages

ISBN:9781450395724

DOI:10.1145/3528114

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 June 2022

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Chongqing University of Technology Postgraduate Innovation Project Fund
Chongqing Municipal Education Commission's Social Network Community Division and Influence Maximization Method Research Project Fund in a Competitive Environment

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DSDE 2022

DSDE 2022: 2022 the 5th International Conference on Data Storage and Data Engineering

February 25 - 27, 2022

Sanya, China

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Cited By

Yin YKou TOchieng NBao X(2023)PASFLN: Positional Association and Semantic Fusion Learning Network for Traffic Object Detection2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC57777.2023.10422508(329-334)Online publication date: 24-Sep-2023
https://doi.org/10.1109/ITSC57777.2023.10422508

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