Ms. Xin Peng Profile

Xin Peng

at Xidian Univ

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Publications (2)

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Proceedings Article | 18 December 2019 Paper

Infrared polymorphic target recognition based on single step cascade neural network

Zhuangzhuang Hu, Hanlin Qin, Xin Peng, Tong Yue, Heng Yue, Guohui Luo, Wenrui Zhu

Proceedings Volume 11342, 113420T (2019) https://doi.org/10.1117/12.2548172

KEYWORDS: Target detection, Target recognition, Neural networks, Feature extraction

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Infared ship recognition has many applications in port supervision and management. However, when the imaging distance is long or the target changes are obvious, it is difficult to achieve accurate detection and recognition by traditional methods. In this paper, we designed a single step cascade neural network that consists of three parts: feature extraction module, scale transform module and classification regression module. Firstly, the VGG network is used to extract the different level features of the target images. Then the scale transform module is used to fuse the high-level features and the low-level features to reflect the semantic information and shallow information of the targets more completely. The generated region of interest is input to classification regression module that predicts the targets location and classes. The main contribution of this paper is to combine the specific problems of infrared polymorphic ships detection and recognition. The clustering algorithm is used to generate the appropriate anchors to adapt our targets, and the attention mechanism is introduced into the model training process. Compared with the traditional detection and recognition methods, the proposed single step cascade neural network achieves the better average precision in polymorphic ships.

Proceedings Article | 18 December 2019 Paper

Gas plume detection in infrared image using mask R-CNN with attention mechanism

Xin Peng, Hanlin Qin, Zhuangzhuang Hu, Binbin Cai, Jin Liang, Hongxuan Ou

Proceedings Volume 11342, 113420U (2019) https://doi.org/10.1117/12.2548179

KEYWORDS: Image segmentation, Edge detection, Target detection, Gases

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The detection of chemical plumes is a challenging task in the field of infrared image detection due to the diffusivity of gas plumes. As a general-purpose segmentation architecture, Mask R-CNN can output high-quality instance segmentation masks while efficiently detecting gases. However, Mask R-CNN cannot achieve accurate segmentation of deformable targets. Therefore, in this paper, an infrared image gas plume detection method based on the attention mechanism Mask R-CNN is proposed, which can effectively detect the gas plume in the image and segment the infrared image. First, the preprocessed image is imported into Feature Pyramid Networks (FPN) to obtain the corresponding feature map. Second, the feature map is sent to the regional offer network (RPN) to obtain candidate RoIs. Then, a ROI Align operation is performed on the candidate ROI. Finally, these ROIs are classified, Bounding-box regression, and Mask generation. And we attach the edge attention mechanism to the mask branch of Mask R-CNN to improve the detection accuracy. The experimental results show that the method is validated on the real infrared gas images, and competitive results with the prior art methods.

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