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Open AccessArticle

Detection of Small Ship Objects Using Anchor Boxes Cluster and Feature Pyramid Network Model for SAR Imagery

1
Navigation College, Dalian Maritime University, Dalian 116026, China
2
Environmental Information Institute, Dalian Maritime University, Dalian 116026, China
3
School of Computer and Software, Dalian Neusoft Information University, Dalian 116023, China
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2020, 8(2), 112; https://doi.org/10.3390/jmse8020112
Received: 6 January 2020 / Revised: 8 February 2020 / Accepted: 10 February 2020 / Published: 12 February 2020
(This article belongs to the Special Issue Maritime Safety)
The synthetic aperture radar (SAR) has a special ability to detect objects in any climate and weather conditions. Consequently, SAR images are widely used in maritime transportation safety and fishery law enforcement for maritime object detection. Currently, deep-learning models are being extensively used for the detection of objects from images. Among them, the feature pyramid network (FPN) uses pyramids for representing semantic information regardless of the scale and has an improved accuracy of object detection. It is also suitable for the detection of multiple small ship objects in SAR images. This study aims to resolve the problems associated with small-object and multi-object ship detection in complex scenarios e.g., when a ship nears the port, by proposing a detection method based on an optimized FPN model. The feature pyramid model is first embedded in a traditional region proposal network (RPN) and mapped into a new feature space for object identification. Subsequently, the k-means clustering algorithm based on the shape similar distance (SSD) measure is used to optimize the FPN. Initial anchor boxes and tests are created using the SAR ship dataset. Experimental results show that the proposed algorithm for object detection shows an accuracy of 98.62%. Compared with Yolo, the RPN based on VGG/ResNet, FPN based on VGG/ResNet, and other models in complex scenarios, the proposed model shows a higher accuracy rate and better overall performance.
Keywords: SAR images; multi-target ship detection; anchor boxes clustering; feature pyramid networks SAR images; multi-target ship detection; anchor boxes clustering; feature pyramid networks
MDPI and ACS Style

Chen, P.; Li, Y.; Zhou, H.; Liu, B.; Liu, P. Detection of Small Ship Objects Using Anchor Boxes Cluster and Feature Pyramid Network Model for SAR Imagery. J. Mar. Sci. Eng. 2020, 8, 112.

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