Vol. 16, No. 11, November 30, 2022
10.3837/tiis.2022.11.002,
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Abstract
In the field of scene segmentation, the precise segmentation of object boundaries in sports movement scene images is a great challenge. The geometric information and spatial information of the image are very important, but in many models, they are usually easy to be lost, which has a big influence on the performance of the model. To alleviate this problem, a parallel dense dilated convolution merging Network (termed PDDCM-Net) was proposed. The proposed PDDCMNet consists of a feature extractor, parallel dilated convolutions, and dense dilated convolutions merged with different dilation rates. We utilize different combinations of dilated convolutions that expand the receptive field of the model with fewer parameters than other advanced methods. Importantly, PDDCM-Net fuses both low-level and high-level information, in effect alleviating the problem of accurately segmenting the edge of the object and positioning the object position accurately. Experimental results validate that the proposed PDDCM-Net achieves a great improvement compared to several representative models on the COCO-Stuff data set.
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Cite this article
[IEEE Style]
D. Huang and L. Zhang, "Parallel Dense Merging Network with Dilated Convolutions for Semantic Segmentation of Sports Movement Scene," KSII Transactions on Internet and Information Systems, vol. 16, no. 11, pp. 3493-3506, 2022. DOI: 10.3837/tiis.2022.11.002.
[ACM Style]
Dongya Huang and Li Zhang. 2022. Parallel Dense Merging Network with Dilated Convolutions for Semantic Segmentation of Sports Movement Scene. KSII Transactions on Internet and Information Systems, 16, 11, (2022), 3493-3506. DOI: 10.3837/tiis.2022.11.002.
[BibTeX Style]
@article{tiis:37996, title="Parallel Dense Merging Network with Dilated Convolutions for Semantic Segmentation of Sports Movement Scene", author="Dongya Huang and Li Zhang and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2022.11.002}, volume={16}, number={11}, year="2022", month={November}, pages={3493-3506}}