Objective

2

Ship detection plays an important role in civil and military fields

including marine object identification

maritime transportation

rescue operation

marine security

and disaster relief. As a basic means of marine monitoring

ship detection in synthetic aperture radar (SAR) images has been studied for years. With the development of sensor and platform technologies

SAR big data are achieved

making it possible to perform automatic data-driven detection algorithms. Deep learning-based detection models have been proven to be a great success in common object detection tasks for natural scene images; moreover

it outperforms many traditional artificial feature based methods. However

when transferring them to SAR ship detection directly

many challenges emerge

and the results are not satisfying because natural and SAR images have several differences. Ship in SAR images usually appear as some bright parts and lack detail information in comparison with natural images because of the coherent imaging mechanism. The swath of SAR remote sensing images is large

but targets are distributed densely or sparsely; thus

the processing of SAR images is usually more complex than that of natural ones. In addition

the size and shape of ship targets vary

ranging from several pixels to hundreds. All these factors complicate ship detection in SAR images. Aiming to solve these challenges and considering the actual demands in practice

this study proposes a lightweight data-adaptive detector with a feature-optimizing mechanism on the basis of the famous single-shot detector (SSD) to improve detection precision and speed.

Method

2

In this study

the original SSD is modified by having the number of channels halved and the last two convolution blocks removed. The settings of the network parameters follow the outputs of proposed data-driven target distribution clustering algorithm

which leans the distributions of targets in the SAR dataset

including the size of ships and the aspect ratio of ships. The algorithm is free from human experience and can make the detector adapt to the SAR dataset. Trunked visual geometry group 16-layer net (VGG16) is utilized to extract features from input SAR images. Given that the features extracted by convolutional neural networks are hierarchical

low-level features with high spatial resolution usually contain extra local and spatial detail information

whereas more semantic and global information are involved in high-level features with low resolution. For object detection tasks

spatial and sematic information are important. Thus

information must be aggregated through a fusion strategy. A new bidirectional feature fusion mechanism

which contains a semantic aggregation and a novel attention guidance module

is proposed. In feature pyramid networks

the higher features are added to the lower features after an upsampling operation. On this basis

the up-sampled higher features in our model are concatenated with lower features in the channel dimension

and the channel numbers are adjusted through a 1×1 convolution operation. Instead of simply adding lower features to higher features

an inverse fusion from down to top and an attention mechanism are applied. A spatial attention map of each convolution block is generated

and the attention map that contains the most spatial information is selected as a weighted map. In the weighted map

target pixels with higher value are usually more noticeable

whereas the value of background pixels are suppressed. After down sample to weight map

element-wise multiplication is performed between the weighted map and the higher features. The features of the targets are strengthened; thus

spatial information is passed to higher level features. The optimized features are then entered into detector heads to predict the locations and types of targets; the low-level features mainly detect the small ships

whereas the high-level features are responsible for the large ones. The entire network is trained by a weighted sum of location and classification losses. In interference

nonmaximum suppression is used for removing repeated bounding boxes.

Result

2

The public SAR ship detection dataset widely used in SAR ship detection references is adopted in experiments. All the experiments are implemented using Python language under the TensorFlow framework on a 64-bit computer with Ubuntu 16.06

CPU Intel (R) Core (TM) i7-6770K @4.00 GHz×8

and NVIDIA GTX 1080Ti with CUDA9.0 and cuDNN7.0 for acceleration. The training iteration

initial learning rate

and batch size are set as 120 k

0.000 1

and 24

respectively. A momentum optimizer is used

with weight decay

gamma

and momentum values of 0.000 5

0.1

and 0.9

respectively. An ablation study is operated to verify the effectiveness of each proposed module

and the model is compared with five published state-of-art methods. Precision rate

recall rate

average precision (AP)

and the average training and testing time on a single image

are taken as evaluation indicators. In the original SSD

a model with parameters from the proposed data-driven target distribution clustering algorithm improves the AP by 1.08% in comparison with the model with original parameters. The lightweight design of the network significantly improves the detection speed; compared with that of the SSD

the training and testing time of the proposed model decrease from 20.79 ms to 12.74 ms and from 14.02 ms to 9.17 ms

respectively. The semantic aggregation and attention fusing modules can improve detection precision

whereas when the two modules are used together

the optimum performance in detection precision is achieved. The AP increased from 77.93% to 80.13%

and the precision and recall rates increased from 89.54% to 96.68% and from 88.60% to 89.60%

respectively. However

speed is not considerably affected

and the model still runs faster than SSD. The proposed model outperforms other models in terms of precision and speed; moreover

it improves AP by 6.9%

1.23%

9.09%

and 2.9% in comparison with other four methods.

Conclusion

2

In this study

we proposed a lightweight data adaptive single shot detector with feature optimizing mechanism. Experiment results show that our model have remarkable advantages over other published state-of-the-art detection approaches in terms of precision and speed.