Maritime security has gained much attention these days because of the frequent interaction between countries with marine borders. Surveillance systems on land are installed on a fixed point, while there is no mounting point for marine surveillance. Fortunately, with the advancement of aerospace technology, the maritime monitoring is possible with continuous satellite image sequences. In this paper, we proposed a complete scheme for maritime surveillance using image sequences on embedded satellite systems, including maritime object detection, tracking, and abnormal activity classification. The recall rate for object detection is 78%. The recall rate for tracking is 91%. The recall rate for anomaly detection is 78%. The FPS is one frame per second on a low power-consuming device installable on a satellite system. The proposed scheme is helpful for the national defense and is labor saving for coast guard affairs.
The rapid development and availability of drones has raised growing interest in their numerous applications, especially for aerial remote-sensing tasks using the Internet of Drones (IoD) for smart city applications. Drones image a large-scale, high-resolution, and no visible band short wavelength infrared (SWIR) ground aerial map of the investigated area for remote sensing. However, due to the high-altitude environment, a drone can easily jitter due to dynamic weather conditions, resulting in blurred SWIR images. Furthermore, it can easily be influenced by clouds and shadow images, thereby resulting in the failed construction of a remote-sensing map. Most UAV remote-sensing studies use RGB cameras. In this study, we developed a platform for intelligent aerial remote sensing using SWIR cameras in an IoD environment. First, we developed a prototype for an aerial SWIR image remote-sensing system. Then, to address the low-quality aerial image issue and reroute the trajectory, we proposed an effective lightweight multitask deep learning-based flying model (LMFM). The experimental results demonstrate that our proposed intelligent drone-based remote-sensing system efficiently stabilizes the drone using our designed LMFM approach in the onboard computer and successfully builds a high-quality aerial remote-sensing map. Furthermore, the proposed LMFM has computationally efficient characteristics that offer near state-of-the-art accuracy at up to 6.97 FPS, making it suitable for low-cost low-power devices.
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