Abstract
Compared with ordinary images, each of the remote sensing images contains many kinds of objects with large scale changes, providing more details. As a typical object of remote sensing image, ship detection has been playing an essential role in the field of remote sensing. With the rapid development of deep learning, remote sensing image detection method based on convolutional neural network (CNN) has occupied a key position. In remote sensing images, the objects of which small scale objects account for a large proportion are closely arranged. In addition, the convolution layer in CNN lacks ample context information, leading to low detection accuracy for remote sensing image detection. To improve detection accuracy and keep the speed of real-time detection, this paper proposed an efficient object detection algorithm for ship detection of remote sensing image based on improved SSD. Firstly, we add a feature fusion module to shallow feature layers to refine feature extraction ability of small object. Then, we add Squeeze-and-Excitation Network (SE) module to each feature layers, introducing attention mechanism to network. The experimental results based on Synthetic Aperture Radar ship detection dataset (SSDD) show that the mAP reaches 94.41%, and the average detection speed is 31FPS. Compared with SSD and other representative object detection algorithms, this improved algorithm has a better performance in detection accuracy and can realize real-time detection.
Highlights
Remote sensing image is the record of all kinds of objects on the ground by artificial satellite
This paper proposes an efficient ship detection algorithm for Synthetic Aperture Radar (SAR) remote sensing images based on improved Single Shot Multibox Detector (SSD)
(2) in order to further improve the ability of feature extraction and raise the significant channel-wise feature as well as reduce insignificant channel-wise feature, a Squeeze-and-Excitation Network (SE) module is added, enabling model to perform dynamic channel-wise feature recalibration
Summary
Remote sensing image is the record of all kinds of objects on the ground by artificial satellite. With the rapid development of highresolution technology, the quality of remote sensing images obtained by remote sensing satellites is getting better and better, which is of great significance to resource exploration, urban traffic management, military object recognition, environmental monitoring and so on. Deep learning has been applied to many fields, including object detection [2], driverless car [3], machine translation [4], emotion recognition [5] and speech recognition [6]. In the field of object detection, a variety of deep learning-based object detection algorithms aiming to resolve practical problems rush out, which are applied to fall detection [7], medical image segmentation [8], defect detection [9], face recognition [10], remote sensing object detection [11] and so on. As an important application in the field of object detection, the massive growth of data promotes the rapid deployment of remote sensing object detection algorithm based on deep learning
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