Abstract
In recent years, object detection has become a popular direction of computer vision and digital image processing. All the research work in this paper is a two-stage object detection algorithm based on deep learning. First, this paper proposes the Deep_Dilated Convolution Network (D_dNet). That is, by adding the operation of dilated convolution into the backbone network, in this way, not only the number of training parameters can be further reduced, but also the resolution of feature map and the size of receptive field can be improved. Second, the Fully Convolutional Layer (FC) is usually involved in the re-identification process of region proposal in the traditional object detection. This too “thick” network structure will easily lead to reduced detection speed and excessive computation. Therefore, the feature map before training is compressed in this paper to establish a light-weight network. Then, transfer learning method is introduced in training network to optimize the model. The whole experiment is evaluated based on MSCOCO dataset. Experiments show that the accuracy of the proposed model is improved by 1.3 to 2.2% points.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (Nos. 61663004, 61762078, 61866004), the Guangxi Natural Science Foundation (Nos. 2016GXNSFAA380146, 2017GXNSFAA198365, 2018GXNSFDA281009), the Research Fund of Guangxi Key Lab of Multi-source Information Mining and Security (16-A-03-02, MIMS18-08), the Guangxi Special Project of Science and Technology Base and Talents (AD16380008), Innovation Project of Guangxi Graduate Education (XYCSZ2019068), the Guangxi “Bagui Scholar” Teams for Innovation and Research Project, Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing.
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Quan, Y., Li, Z., Zhang, F., Zhang, C. (2019). D_dNet-65 R-CNN: Object Detection Model Fusing Deep Dilated Convolutions and Light-Weight Networks. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11672. Springer, Cham. https://doi.org/10.1007/978-3-030-29894-4_2
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