Objective

2

Synthetic aperture radar(SAR) imaging is interfered with the echo due to electromagnetic waves penetrate the rough object surface by emitting electromagnetic pulses and receiving reflected echo imaging

resulting in coherent spot noise. However

its multi-temporal and all-weather features are used in maritime applications like rescue

monitoring and vessel transportation supervision. Constant false alarm rate algorithm is use for SAR image target detection in common. It has the simple algorithm structure

but the complex and uneven distribution of ocean clutter types can cause missing detection. In the case of multiple targets

it is easy to appear that the reference unit contains target information

so the amplitude of target signal may be greater than the clutter amplitude

resulting in the estimated threshold is far greater than the real threshold. The constant false alarm rate algorithm cannot obtain the ideal detection effect. In recent years

with the rapid development of deep learning

object detection algorithms based on deep learning have achieved good results in optical image object detection. SAR images are different from optical images in that the targets in SAR images are mostly small-scale ships with bright spots and lack obvious details. Similar targets such as islands

buildings and so on will form similar ship shapes after electromagnetic wave reflection. Therefore

the application of optical detection models directly to SAR images cannot achieve ideal results. Feature pyramid network adopts top-down layer by layer fusion

so the high-level feature map lacks spatial location information

and the fusion of each level largely loses the semantic information contained in high-level features

while the semantic information of low-level features is insufficient. In order to improve the model detection accuracy and make the model more robust

a SAR image detection model with adaptive weight pyramid and branch strong correlation is proposed in this paper.

Method

2

The high-level features extracted by ResNet101 contain rich semantic information

and the underlying features contain rich spatial information. To have a better detection effect of target detection model

the feature map is required to contain semantic information and spatial location information both. Therefore

the featured information needs to be adopted based on a more effective fusion mechanism for fusion. It is necessary to use effective weights to guide the fusion mechanism for to the integration of more effective information. Our adaptive weight pyramid module is demonstrated

which first samples the feature map to make it have the same scale

then stitches concatenation the feature map after each layer of sampling into channel dimensions

adjusts the number of channels by 1×1 convolution

and generates the weight in the end through the Softmax function. To get a feature map of the layer

multiply each layer feature by the corresponding weight

and the other layers are analogy. Its fusion removes some redundant information under the weighting of machine learning. Multiple features are extracted through 1×1 and 3×3 symmetric convolution kernels and 1×3 and 3×1 asymmetric convolution kernels

following the feature fusion of classification branch and regression branch by the branch correlation module. The feature diagram contains the classification branch information in the regression branch based on the fused classification and regression features. In the detection phase

the candidate boxes are removed related to accurate positioning and low classification confidence

which reduces the detection accuracy of the model greatly. In order to solve this problem

the intersection over union(IoU) detection head is constructed in the regression branch

and IoU it is used for classification branch

thus avoiding the problem of the candidate box with high IoU low classification confidence being suppressed. In order to balance the IoU branch and the classification branch

the constrained balance factor is designed sand can guide the regression branch optimization candidate box better.

Result

2

Our model is evaluated on the public remote sensing dataset SSDD(SAR ship detection)

which had 1 160 pictures

including 2 456 vessel targets. The dataset is divided dataset into training sets and test sets by a ratio of 7 : 3

and enhanced the learning ability of the model by rotating

flipping

brightness and increasing Gaussian noise. All the experimental environments are based on ubuntu16.04 LTS operating system

equipped with CPU for lntel (R) Core (TM) i7-7700@3.6 GHz ×8

graphics card for NVIDIA GTX1080Ti memory for 11 GB

running under the Tensorflow framework

through CUDA8.0 and cuDNN5.0 accelerated training. Our model learning rate is set to 0.000 5

the learning rate is attenuated by 1/10 per 40 k iterations

and the network is convergent at 80 k iterations completely. The non-maximum suppression threshold is 0.5 and the predicted probability threshold is 0.6. The recall rate

accuracy rate

average accuracy

F1 value and detection speed are represented as the evaluation indexes of the model. The model realizes the best detection effect in terms of equilibrium factor takes 0.4. The final recall rate is increased from 89.17% to 93.63%

the accuracy rate is increased from 87.94% to 95.08%

the average accuracy is increased from 86.91% to 90.53%

F1 value is increased from 88.55% to 94.53%

and the detection speed reached 20.14 frame/s.

Conclusion

2

Our experimental results demonstrate that the feature map targets fused by adaptive weight pyramids are more prominent

and the strong correlated branches module enhances the correlation between the classification branch and the regression branch

and improves the candidate box regression ability. Our model has it potential to meet the real-time detection requirement of SAR imaging.