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Proceedings Paper

Feature enhanced faster R-CNN for object detection
Author(s): Jun Jiang; Zhongbing Hu
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Paper Abstract

In recent years, deep convolutional neural networks (CNNs) have achieved great successes in object detection, however, feature extraction is still sensitive to scale variation. FPN is one of the majority strategies to deal with this problem. It uses a top-down pathway and lateral connection to combine high-level features with low-level features, and then generates robust features. However, in FPN, high-level features are still unable to capture the detail information, and this results in the inconsistent representations for the same objects with different scales. To solve this problem, we proposed a Feature Enhanced Module to get more robust features, which can help the networks to produce object localization with higher quality, i.e., without bells and whistles. The performance of the proposed method is shown by the experiments in which it achieves a 1.1 point AP50 gain and 2.3 point AP75 gain on the Pascal VOC dataset, comparing to the Faster RCNN with FPN.

Paper Details

Date Published: 14 February 2020
PDF: 7 pages
Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 114290S (14 February 2020); doi: 10.1117/12.2539211
Show Author Affiliations
Jun Jiang, National Key Lab. of Science & Technology on Multispectral Information Processing (China)
Key Lab. of Image Processing and Intelligent Control (China)
Huazhong Univ. of Science and Technology (China)
Zhongbing Hu, National Key Lab. of Science & Technology on Multispectral Information Processing (China)
Key Lab. of Image Processing and Intelligent Control (China)
Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 11429:
MIPPR 2019: Automatic Target Recognition and Navigation
Jianguo Liu; Hanyu Hong; Xia Hua, Editor(s)

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