Simultaneous Ship Detection and Orientation Estimation in SAR Images Based on Attention Module and Angle Regression

Abstract

Ship detection and angle estimation in SAR images play an important role in marine surveillance. Previous works have detected ships first and estimated their orientations second. This is time-consuming and tedious. In order to solve the problems above, we attempt to combine these two tasks using a convolutional neural network so that ships may be detected and their orientations estimated simultaneously. The proposed method is based on the original SSD (Single Shot Detector), but using a rotatable bounding box. This method can learn and predict the class, location, and angle information of ships using only one forward computation. The generated oriented bounding box is much tighter than the traditional bounding box and is robust to background disturbances. We develop a semantic aggregation method which fuses features in a top-down way. This method can provide abundant location and semantic information, which is helpful for classification and location. We adopt the attention module for the six prediction layers. It can adaptively select meaningful features and neglect weak ones. This is helpful for detecting small ships. Multi-orientation anchors are designed with different sizes, aspect ratios, and orientations. These can consider both speed and accuracy. Angular regression is embedded into the existing bounding box regression module, and thus the angle prediction is output with the position and score, without requiring too many extra computations. The loss function with angular regression is used for optimizing the model. AAP (average angle precision) is used for evaluating the performance. The experiments on the dataset demonstrate the effectiveness of our method.

Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection

“…SAR images in this dataset possess different satellite sensors, various polarization modes, multiple resolutions, different scenes, and abundant ship sizes, so it can verify the robustness of methods. Therefore, many scholars [10,21,35,[55][56][57][58][59][60][61][62][63] conducted research based on it for a better comparison.…”

“…We also compared our methods with some previous open studies which use the same SSDD dataset. To be clear, here, we replaced NVIDIA RTX2080Ti GPU with NVIDIA GTX1080 GPU to keep the hardware environment basically the same as previous other open studies (References [10,21,35,[59][60][61][62] used NVIDIA GTX1080 GPU.). For different performances of different types of GPUs, we have to make such a replacement in order to consider the rationality of the comparison experiment.…”

LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images

“…(4) In fact, there is still a lot of resistance to obtain the physical size of the ship (i.e., the ship length L and the ship breadth B), because it is difficult to obtain these accurate information comprehensively from the limited AIS data (i.e., there are still some "dark" ships [9,92] that fish illegally, smuggle in illegally, etc., which cannot be monitored by AIS, whose ship length L and ship breadth B cannot be obtained; see more detail in references [9,92]). Given the above, it does not affect the core work of this paper if not using the ship length L and ship breadth B, because (1) the width and height of a ship ground truth rectangular box have been able to describe ship pixel size well, merely not containing more information when compared to the ship length L and ship breadth B; (2) different from the ship recognition or classification task in OpenSARShip [80] that may need specific the ship length L and ship breadth B to represent ship features to enhance recognition accuracy, our LS-SSDD-v1.0 only focus on ship location, which means just using a simple rectangle box to frame the ship; and (3) similar to the other datasets, we only use a vertical or horizontal rectangular box to locate the center point of the ship, not considering the direction estimation of rotatable boxes in references [11,93] that may be involved with the ship length L and ship breadth B.…”

Geospatial Contextual Attention Mechanism for Automatic and Fast Airport Detection in SAR Imagery

“…Attention module plays an important role on targets classification and detection [33]. Wang et al [34] and Cui et al [35] both combined channel attention and spatial attention to detect ships. However, dual attention brings additional parameters, which greatly increases the training time of the neural network.…”