Abstract
Intelligent detection and recognition of ships from high-resolution remote sensing images is an extraordinarily useful task in civil and military reconnaissance. It is difficult to detect ships with high precision because various disturbances are present in the sea such as clouds, mist, islands, coastlines, ripples, and so on. To solve this problem, we propose a novel ship detection network based on multi-layer convolutional feature fusion (CFF-SDN). Our ship detection network consists of three parts. Firstly, the convolutional feature extraction network is used to extract ship features of different levels. Residual connection is introduced so that the model can be designed very deeply, and it is easy to train and converge. Secondly, the proposed network fuses fine-grained features from shallow layers with semantic features from deep layers, which is beneficial for detecting ship targets with different sizes. At the same time, it is helpful to improve the localization accuracy and detection accuracy of small objects. Finally, multiple fused feature maps are used for classification and regression, which can adapt to ships of multiple scales. Since the CFF-SDN model uses a pruning strategy, the detection speed is greatly improved. In the experiment, we create a dataset for ship detection in remote sensing images (DSDR), including actual satellite images from Google Earth and aerial images from electro-optical pod. The DSDR dataset contains not only visible light images, but also infrared images. To improve the robustness to various sea scenes, images under different scales, perspectives and illumination are obtained through data augmentation or affine transformation methods. To reduce the influence of atmospheric absorption and scattering, a dark channel prior is adopted to solve atmospheric correction on the sea scenes. Moreover, soft non-maximum suppression (NMS) is introduced to increase the recall rate for densely arranged ships. In addition, better detection performance is observed in comparison with the existing models in terms of precision rate and recall rate. The experimental results show that the proposed detection model can achieve the superior performance of ship detection in optical remote sensing image.
Highlights
The intelligent detection and recognition of ships is quite important for maritime security and civil management
Compared to synthetic aperture radar (SAR) images, the information provided by optical remote sensing images is more intuitive, so it is easy for humans to understand [3]
To verify the superiority of the method we proposed, we compare the performance of our model with other state-of-the-art natural image object detection frameworks, such as Faster Regions Convolution Neural Network (Faster R-CNN), Single Shoot MultiBox Detector (SSD), You Only Look Once v3: An Incremental Improvement (YOLOv3)
Summary
The intelligent detection and recognition of ships is quite important for maritime security and civil management. Satellite and aerial remote sensing technology has developed rapidly, and optical remote sensing images can provide detailed information with extremely high resolution [2]. Ship detection has become a hot topic in the field of optical remote sensing. SAR can work under all weather conditions and various climatic conditions, so ship detection is mostly completed in the SAR images. Compared to SAR images, the information provided by optical remote sensing images is more intuitive, so it is easy for humans to understand [3]. Numerous satellites and unmanned aerial vehicles (UAVs) have made it possible to obtain massive high-resolution optical remote sensing images on the sea. We can obtain more detailed information to detect the ship in the optical remote sensing images. This work mainly faces following three challenges due to the complicate background:
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