Journal of System Simulation
Abstract
Abstract: A improved vehicle detection model based on multi-scale feature fusion of YOLOX network is proposed to solve the problem of missing and false detection of small vehicle targets. Ghost-cross stage partial(CSP) based on the depth separable convolution is designed to replace part of cross stage partial in network to speed up the speed of detection. The max pooling mode of model is improved to Softpool mode, and coordinate attention mechanism is introduced to enhance the feature expression of target to be detected and to optimize the problem of target missing detection. Focal Loss is selected as the confidence loss function of model to increase the weight of inaccurate classification samples and improve the prediction ability of the model for small targets. The experimental results show that the average accuracy of the improved algorithm is improved to 74.96%, and the speed is up to 73 frames per second, which can better meet the requirements of real-time vehicle target detection.
Recommended Citation
Zhang, Xiliu; Zhang, Xiaoling; and He, Minjun
(2024)
"Research on Vehicle Detection Method Based on Improved YOLOX-s,"
Journal of System Simulation: Vol. 36:
Iss.
2, Article 17.
DOI: 10.16182/j.issn1004731x.joss.22-1106
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol36/iss2/17
First Page
487
Last Page
496
CLC
TP391
Recommended Citation
Zhang Xiliu, Zhang Xiaoling, He Minjun. Research on Vehicle Detection Method Based on Improved YOLOX-s[J]. Journal of System Simulation, 2024, 36(2): 487-496.
DOI
10.16182/j.issn1004731x.joss.22-1106
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