A Hybrid Model and Data-Driven Vision-Based Framework for the Detection, Tracking and Surveillance of Dynamic Coastlines Using a Multirotor UAV

Abstract

A hybrid model-based and data-driven framework is proposed in this paper for autonomous coastline surveillance using an unmanned aerial vehicle. The proposed approach comprises three individual neural network-assisted modules that work together to estimate the state of the target (i.e., shoreline) to contribute to its identification and tracking. The shoreline is first detected through image segmentation using a Convolutional Neural Network. The part of the segmented image that includes the detected shoreline is then fed into a CNN real-time optical flow estimator. The position of pixels belonging to the detected shoreline, as well as the initial approximation of the shoreline motion, are incorporated into a neural network-aided Extended Kalman Filter that learns from data and can provide on-line motion estimation of the shoreline (i.e., position and velocity in the presence of waves) using the system and measurement models with partial knowledge. Finally, the estimated feedback is provided to a Partitioned Visual Servo tracking controller for autonomous multirotor navigation along the coast, ensuring that the latter will always remain inside the onboard camera field of view. A series of outdoor comparative studies using an octocopter flying along the shoreline in various weather and beach settings demonstrate the effectiveness of the suggested architecture.

Malicious UAV Detection Over Rician Fading Channel: Performance Analysis

“…Recently, the aid of computer vision in target detection and tracking has developed rapidly due to its ability to provide high performance systems [30]- [32]. In [9], a Dalian University of Technology (DUT) Anti-UAV dataset was used on several existing detection algorithms for performance evaluation and comparison with a proposed detection and tracking algorithm that showed high performance by experiments.…”

Dual-Channel Semi-Supervised Adversarial Network for Building Segmentation from UAV-Captured Images

“…Optical flow estimation tracks pixel movements and changes within an image, thereby proving invaluable for tackling a multitude of segmentation challenges, including foregroundbackground separation, object tracking, and boundary detection [22]. By analyzing pixel displacements within an image, optical flow estimation enhances the understanding of both the structural layout and motion dynamics within the image [23]. The challenges in building segmentation include sensitivity to illumination and weather conditions, and without constraints on optical flow estimation, it is difficult to capture complex textures and precise shapes [24].…”