Improved YOLOX object detection algorithm based on gradient difference adaptive learning rate optimization

Yucun SONG; Quanbo GE; Junlong ZHU; Zhenyu LU

doi:10.7527/S1000-6893.2022.27951

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2023 , Vol. 44 >Issue 14: 327951 - 327951

DOI: https://doi.org/10.7527/S1000-6893.2022.27951

Electronics and Electrical Engineering and Control

Improved YOLOX object detection algorithm based on gradient difference adaptive learning rate optimization

Yucun SONG ,
Quanbo GE ,
Junlong ZHU ,
Zhenyu LU

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^1.School of Artificial Intelligence/School of Future Technology，Nanjing University of Information Science and Technology，Nanjing 　210044，China
^2.School of Automation，Nanjing University of Information Science and Technology，Nanjing 　210044，China
^3.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology （CICAEET），Nanjing University of Information Science and Technology，Nanjing 　210044，China
^4.Jiangsu Province Engineering Research Center of Intelligent Meteorological Exploration Robot （C?IMER），Nanjing University of Information Science and Technology，Nanjing 　210044，China
^5.College of Information Engineering，Henan University of Science and Technology，Luoyang 　471000，China

E-mail： qbge_tju@163.com

Received date: 2022-08-29

Revised date: 2022-10-18

Accepted date: 2022-11-09

Online published: 2022-11-17

Supported by

National Natural Science Foundation of China(62033010);Aeronautical Science Foundation of China(2019460T5001)

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Abstract

Object detection has always been one of the most challenging problems in the field of computer vision， and is widely used in the tasks such as face recognition， autonomous driving and traffic detection. To further improve the performance of current mainstream object detection algorithms， this paper proposes an improved object detection algorithm based on YOLOX， and carries out experiments on the standard PASCAL VOC 07+12 and RSOD datasets. The YOLOX object detection algorithm is improved mainly through data enhancement， improving network structure and loss function. At the same time， an adaptive learning rate optimization algorithm based on gradient difference is proposed to train the improved YOLOX algorithm， which is also suitable for optimization of other neural networks. Experiments are carried out on PASCAL VOC 07+12 standard data sets. Results show that the AP of the improved YOLOX-S algorithm is increased from 61.63% to 66.35% compared with that of the original YOLOX-S algorithm. The improvement effect is obvious. Experiments are also carried out on the RSOD standard data set. The results show that the AP of the improved YOLOX-S algorithm is increased from 69.4% to 73.2% on the RSOD data set， compared with those of other mainstream YOLO series algorithms. The improvement effect is also significant. Experiments show effective improvement of YOLOX’s object detection.

Key words： object detection; YOLOX; neural network optimization; PASCAL VOC; RSOD

Cite this article

Yucun SONG , Quanbo GE , Junlong ZHU , Zhenyu LU . Improved YOLOX object detection algorithm based on gradient difference adaptive learning rate optimization[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023 , 44(14) : 327951 -327951 . DOI: 10.7527/S1000-6893.2022.27951

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