Mr. Jingjing Fei Profile

Jingjing Fei

at Tongji Univ

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Proceedings Article | 14 February 2020 Paper

ATSGPN: adaptive threshold instance segmentation network in 3D point cloud

Yu Sun, Zhicheng Wang, Jingjing Fei, Ling Chen, Gang Wei

Proceedings Volume 11430, 114301O (2020) https://doi.org/10.1117/12.2541582

KEYWORDS: Image segmentation, Feature extraction, 3D modeling, Neural networks, 3D image processing

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We introduce an adaptive threshold instance segmentation network in point cloud based on similarity group proposal network(SGPN), named adaptive threshold similarity group proposal network(ATSGPN). SGPN learns the feature of point cloud to process similarity matrix and clusters. In our experiments, we find that we cannot always get the proper threshold by heuristic method to divide the points although the similarity matrix is good enough. Based on this idea, we introduce the Threshold Map to learn segmentation threshold. We also improve the feature extraction using edge convolution(EdgeConv). The point cloud first passes EdgeConv to extract features and learns the similarity matrix in feature space. The semantic label of each point and the segmentation threshold can help to generate groups and then calculates confidence to evaluate the group quality and backpropagation. ATSGPN has higher accuracy on Stanford Large- Scale 3D Indoor Spaces Dataset (S3SID) and fewer steps than SGPN, and there are some experiments can be shown in the paper for its good performance.

Proceedings Article | 14 February 2020 Paper

Multi-scale oriented object detection in aerial images based on convolutional neural networks with global attention

Jingjing Fei, Zhicheng Wang, Zhaohui Yu, Xi Gu, Gang Wei

Proceedings Volume 11432, 114320K (2020) https://doi.org/10.1117/12.2541855

KEYWORDS: Convolutional neural networks, Image classification, Image fusion, Computer aided design, Neural networks, Network architectures, Feature extraction, Visualization

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Object detection is a fundamental yet challenging problem in natural scenes and aerial scenes. Although region based deep convolutional neural networks (CNNs) have brought impressive improvements for object detection in natural scenes, detecting oriented objects in aerial images still remains challenging, due to the complexity of the aerial image backgrounds and the large degree of freedom in scale, orientation, and density. To tackle these problems, we propose a novel network, composed of backbone structure with global attention module, multi-scale object proposal network and final oriented object detector, which can efficiently detect small objects, arbitrary direction objects, and dense objects in aerial images. We utilize pyramid pooling blocks as a global attention module on the top of the backbone structure to generate discriminative feature representations, which provide diverse context information and complementary receptive field for the detector. The global attention module can help the model reduce false alarms and incorrect classifications in the complex aerial image backgrounds. The multi-scale object proposal network aims to generate object-like regions at different scales through several intermediate layers. After that, these regions are sent to the detector for refined classification and regression, which can alleviate the problem of variant scales in aerial images. The oriented object detector is designed to generate predictions for inclined box. The quantitative comparison results on the challenging DOTA dataset show that our proposed method is more accurate than baseline algorithms and is effective for objection detection in aerial images. The results demonstrate that the proposed method significantly improves the performance.

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