Objective

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Saliency in the detection of aerial remote sensing image can have many military and life applications. On the one hand

the spatial resolution of remote sensing image is becoming higher with the improvement of technology. On the other hand

it can be applied in urban traffic planning

military target tracking

ground object classification

and other aspects. Most of the advanced target detection algorithms (such as Fast region with convolutional neural network (R-CNN)

Mask R-CNN

and single shot multibox detector (SSD)) are tested on the general data set. However

the classifier based on the training of the general data set does not have a good detection effect on the aerial remote sensing image primarily due to the particularity of the aerial remote sensing image. An aerial remote sensing image is taken from a height of several hundred meters or even up to 10 000 m due to scale diversity. Thus

the sizes of similar objects in the remote sensing image differ. Taking the ship in the port as an example

the super large ship is nearly 400 meters long

and the small ship is tens of meters long. Aerial remote sensing images are shot from a high-altitude perspective

and the objects presented are all top views

which are quite different from the data set (horizontal perspective) generally used due to the particularity of perspective

which will lead to the poor effect of the trained target detection algorithm in practical application of remote sensing images. In the small target problem

most of the targets in the aerial remote sensing image are small (tens of pixels or even several pixels)

the amount of information of these targets in the image is very small

and the mainstream target detection algorithm is not ideal for the detection effect of small targets in these remote sensing images mainly because the detection method based on convolutional neural network uses the pooling layer

resulting in a lower original amount of information. For example

the target image of 24×24 pixels is transformed into 1×1 pixel after four pooling layers

and the dimension is very low to be classified. The background complexity is high because the aerial remote sensing image is taken from a high altitude

its field of vision is relatively large (usually covers several square kilometers)

and the image contains tens of thousands of backgrounds

resulting in the integration of the background and the small target

which has a strong interference on detection. Generally

the recognition rate of a small target in the remote sensing image is low

the scale is diverse

the direction is disordered

and the background is complex. On the one hand

edge information is lost when a small target is pooled. On the other hand

the semantic information of the feature map is not strong enough to detect the corresponding target. In this paper

a parallel high-resolution network structure combined with long short-term memory (LSTM) is proposed to replace the basic detection network visual geometry group 16-layer net (VGG16) of SSD and improve the detection accuracy of the algorithm for aerial targets.

Method

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This paper introduces high-resolution network (HRNet) network and LSTM network in the SSD model. The largest feature of the HR-Net parallel network is that the input image can always maintain a high-resolution output. This parallel network structure and traditional top-down extraction feature are then up sampled and restored. The feature size is different. The parallel structure effectively reduces the number of down sampling and the loss of feature information of the target edge to be detected. The LSTM network is a variant of the circulatory neural network. The R-CNN cannot be deeply trained due to the disappearance of the gradient. The LSTM network combines short-term memory with long-term memory through subtle door control

which solves the gradient disappearance to a certain extent. To address the problem of gradient explosion

first

the method of parallel high-resolution feature map in HRNet is used to build the residual module. The first stage is the high-resolution subnetwork

which gradually increases the high-resolution subnetwork to the low-resolution subnetwork

and the multistage subnetwork is connected in parallel. Second

repeated feature fusion is carried out to obtain rich feature information. Finally

the feature map of each subnet is sampled and fused

the channel information is integrated with bidirectional LSTM

and context information is effectively used to form a multiscale detection.

Result

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By applying the improved network to SSD algorithm

this paper compares it with the SSD method on common objects in context (COCO) 2017 dataset

Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI)

and University of Chinese Academy of Sciences-Aerial Object Detection (UCAS-AOD) of aviation target dataset. In the COCO2017 dataset

the model mean average precision is 41.6%

which is 10.4% higher than that of SSD513 + ResNet101. In the KITTI and UCAS-AOD datasets

the mean average precision (mAP) of this model is 69.4% and 69.3%

respectively. On COCO2017 dataset

KITTI dataset and UCAS-AOD dataset

the average detection accuracy of this algorithm increased by 10.4%

7.3% and 8.8% compared with SSD513.

Conclusion

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Results show that this method can reduce the miss detection rate of a small target and improve the average detection accuracy of the entire target.