Ms. Zhaoying Liu Profile

Zhaoying Liu

at Beijing Univ of Technology

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Publications (2)

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Proceedings Article | 12 October 2022 Paper

Ship target detection based on multitask learning

Junran He, Ting Zhang, Zhaoying Liu, Yujian Li

Proceedings Volume 12342, 123420H (2022) https://doi.org/10.1117/12.2644430

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Ship target detection is of great significance in marine surveillance, rescue and so on. In this paper, in order to improve the performance of ship target detection, we proposed a ship target detection method based on multi task learning. There are mainly two contributions. Firstly, we designed a multi-task learning model by integrating segmentation module to the faster RCNN model. Through the strategies of feature sharing and joint learning, it is helpful to improve the accuracy of target detection with the assistance of segmentation; Secondly, in order to deal with the impact of initial anchor frame scale on target detection accuracy, we introduced an adaptive anchor width height ratio setting method based on improved K-means algorithm, by adaptively select initial anchor size suitable for the characteristics of ship targets, it is beneficial to further improve the detection accuracy. Moreover, we constructed an extended version of ship image data set including 14614 images belonging to 13 categories. Experimental results demonstrated that the proposed model can effectively improve the accuracy of ship target detection; and the comparison and the ablation experiments further validated the strategies of multi-task joint learning and adaptive anchor size setting is helpful for improving the performance of ship target detection.

Proceedings Article | 12 October 2022 Paper

Fine-grained birds recognition based on lightweight bilinear CNN with Additive Margin Softmax

Yuchen Sun, Bingchen Shen, Ziqiao Jin, Zhaoying Liu

Proceedings Volume 12342, 123420T (2022) https://doi.org/10.1117/12.2644253

KEYWORDS: Convolution, Visual process modeling, Performance modeling, Neural networks, Data modeling, Image classification, Feature extraction, Network architectures, Image segmentation, Image retrieval

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We propose a novel LBCNN model with AM Softmax based on bilinear CNN (BCNN) and AM Softmax loss function, which can better fit fine-grained birds recognition tasks. There are mainly two contributions. Firstly, in order to reduce the model size and recognition time, we design a lightweight BCNN model to reduce the parameters. We replace original VGG16 backbone with MobileNet structure which decomposes the convolution operation into two smaller operations: depthwise revolution and pointwise revolution. Secondly, to make up for the decrease in accuracy, we introduce the Additive Margin Softmax (AM Softmax) loss function to enhance the discrimination ability. By comprehensive discussion of the influence of different parameter settings and different loss functions, we test the proposed lightweight BCNN on the bird dataset CUB-200-2011. Experimental results demonstrate that the proposed model can achieve comparable results with much fewer parameters.

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