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Proceedings Paper

Remote multi-object detection based on bounding box field
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Paper Abstract

This paper proposes a new irregular remote sensing object detection algorithm that different from the ROI or rotating BOX obtained by traditional one. The architecture is designed to jointly learn four bounding box corner points and their association via two branches of the same sequential prediction process. The algorithm predicts four key points of the object and their associated connection, Bounding Box Fields(BBF) via convolutional neural network(CNN), and thus obtains the detail spatial distribution of the objects.

In order to improve the positioning accuracy of the key points, network architecture reduced Receptive Field from large to small stage by stage. It has achieved ROI free finally. In this method, the object detection problem is framed as CNN convolution point detection and bounding box field detection, it achieved the one stage object detection with high precision and high speed.

We verified the effectiveness and efficiency of the algorithm through experiments, which proved that the new data structure could locate the object attitude and spatial direction more accurately in real time with strong practicability.

Paper Details

Date Published: 14 February 2020
PDF: 8 pages
Proc. SPIE 11428, MIPPR 2019: Multispectral Image Acquisition, Processing, and Analysis, 114280F (14 February 2020); doi: 10.1117/12.2541916
Show Author Affiliations
Jin Liu, Wuhan Univ. (China)
RongHao Li, Wuhan Univ. (China)
YongJian Gao, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 11428:
MIPPR 2019: Multispectral Image Acquisition, Processing, and Analysis
Xinyu Zhang; Chao Pan; Hongshi Sang, Editor(s)

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