Tiny object detection using multi-feature fusion

Peng Yang; Yuejin Zhao; Ming Liu; Liquan Dong; Xiaohua Liu; Mei Hui

doi:10.1117/12.2541898

14 February 2020 Tiny object detection using multi-feature fusion

Peng Yang, Yuejin Zhao, Ming Liu, Liquan Dong, Xiaohua Liu, Mei Hui

Proceedings Volume 11429, MIPPR 2019: Automatic Target Recognition and Navigation; 1142916 (2020) https://doi.org/10.1117/12.2541898
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

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.

Citation Download Citation

Peng Yang, Yuejin Zhao, Ming Liu, Liquan Dong, Xiaohua Liu, and Mei Hui "Tiny object detection using multi-feature fusion", Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 1142916 (14 February 2020); https://doi.org/10.1117/12.2541898

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KEYWORDS

Target detection

Satellites

Satellite imaging

Image fusion

Earth observing sensors

Image processing

Detection and tracking algorithms

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