Paper Info
Title
Deformable Feature Pyramid Network for Ship Recognition.
Abstract
Ship recognition under complex sea environment and weather condition is a challenging task because the ship appearances change greatly, especially under large geometric transformation. The original feature pyramid network (FPN) [4] does not achieve good performance if it is implemented on ship detection directly, because it uses the fixed geometric structures in their building modules. In this paper, a deformable feature pyramid network is designed for ship recognition. The contributions are three folds: (1) We change the fixed geometric structure model of the original feature pyramid network to deformable geometric structure model and use the dilated convolution [12] instead of the original convolution. Correspondingly, deformable position-sensitive RoI pooling is used instead of the fixed geometric RoI pooling in the RoI-wise subnetwork. (2) The focal loss function [6] replaces the original mixed cross-entropy loss function. (3) Decay-NMS, a new post-processing method, is designed in this paper to improve the detection accuracy. The experimental results demonstrate the effectiveness and efficiency of our model.
Year
DOI
Venue
2018
10.1007/978-3-030-00764-5_6
ADVANCES IN MULTIMEDIA INFORMATION PROCESSING, PT III
Keywords
Field
DocType
Ship recognition,Deformable feature pyramid network,Dilated convolution,Deformable position-sensitive RoI pooling,Focal loss,Decay-NMS
Computer vision,Pattern recognition,Convolution,Computer science,Pooling,Geometric transformation,Pyramid,Artificial intelligence,Subnetwork,Weather condition
Conference
Volume
ISSN
Citations 
11166
0302-9743
0
PageRank 
References 
Authors
0.34
11
4
Name
Order
Citations
PageRank
Yao Ding100.68
Yichen Zhang26711.87
Yanyun Qu321638.66
Cui-Hua Li47413.24