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Proceedings Paper

Tiny object detection using multi-feature fusion
Author(s): Peng Yang; Yuejin Zhao; Ming Liu; Liquan Dong; Xiaohua Liu; Mei Hui
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Paper Abstract

Vehicle identification is widely used in route planning, safety supervision and military reconnaissance. It is one of the research hotspots of space-based remote sensing applications. Traditional HOG, Gabor features and Hough transform and other manual design features are not suitable for modern city satellite data analysis. With the rapid development of CNN, object detection has made remarkable progress in accuracy and speed. However, in satellite map analysis, many targets are usually small and dense, which results in the accuracy of target detection often being half or even lower than the big target. Small targets have lower resolution, blurred images, and very rare information. After multi-layer convolution, it is difficult to extract effective information. In the satellite map data set we produced, the target vehicles are not only small but also very dense, and it is impossible to achieve high detection accuracy when using YOLO for training directly. In order to solve this problem, we propose a multi-feature fusion target detection method, which combines satellite image and electronic image to achieve the fusion of target vehicle and surrounding semantic information. We conducted a comparative experiment to demonstrate the applicability of multi-feature fusion methods in different detection models such as YOLO and R-CNN. By comparing with the traditional target detection model, the results show that the proposed method has higher detection accuracy.

Paper Details

Date Published: 14 February 2020
PDF: 7 pages
Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 1142916 (14 February 2020); doi: 10.1117/12.2541898
Show Author Affiliations
Peng Yang, Beijing Institute of Technology (China)
Yuejin Zhao, Beijing Institute of Technology (China)
Ming Liu, Beijing Institute of Technology (China)
Liquan Dong, Beijing Institute of Technology (China)
Xiaohua Liu, Beijing Institute of Technology (China)
Mei Hui, Beijing Institute of 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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