Rotated region based CNN for ship detection

The state-of-the-art object detection networks for natural images have recently demonstrated impressive performances. However the complexity of ship detection in high resolution satellite images exposes the limited capacity of these networks for strip-like rotated assembled object detection which ar...

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Bibliographic Details
Published in2017 IEEE International Conference on Image Processing (ICIP) pp. 900 - 904
Main Authors Liu, Zikun, Hu, Jingao, Weng, Lubin, Yang, Yiping
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2017
Subjects
Convolution
convolutional neural network
Feature extraction
Marine vehicles
Object detection
Proposals
Rotated region
ship detection
Task analysis
Training
Online AccessGet full text
ISSN2381-8549
DOI10.1109/ICIP.2017.8296411

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Summary:The state-of-the-art object detection networks for natural images have recently demonstrated impressive performances. However the complexity of ship detection in high resolution satellite images exposes the limited capacity of these networks for strip-like rotated assembled object detection which are common in remote sensing images. In this paper, we embrace this observation and introduce the rotated region based CNN (RR-CNN), which can learn and accurately extract features of rotated regions and locate rotated objects precisely. RR-CNN has three important new components including a rotated region of interest (RRoI) pooling layer, a rotated bounding box regression model and a multi-task method for non-maximal suppression (NMS) between different classes. Experimental results on the public ship dataset HRSC2016 confirm that RR-CNN outperforms baselines by a large margin.
ISSN:2381-8549
DOI:10.1109/ICIP.2017.8296411