Research on Drone Swarm Networking Inspection and Intelligent Monitoring for Seawall Engineering

To address the inherent limitations of conventional seawall inspection methods, including restricted spatial coverage, delayed response to potential hazards, inefficient data sharing mechanisms, and inadequate target recognition accuracy, this study integrates edge computing with cloud-edge collaborative architectures to develop an intelligent perception and monitoring system based on a networked unmanned aerial vehicle (UAV) swarm. Traditional inspection approaches, which rely heavily on manual patrols or single-platform monitoring systems, often struggle to meet the increasing demands for real-time, large-scale, and high-precision assessment of coastal defense infrastructure. In contrast, the proposed system is specifically designed to support continuous and automated monitoring of abnormal conditions, such as seawall deformation, surface erosion, and structural damage, in complex coastal environments. At the system level, a novel cooperative path planning strategy, referred to as CTCP-A* (Cooperative Trajectory Constraint Pruning-A*), is introduced to coordinate multi-UAV operations. By incorporating trajectory constraint pruning into the classical A* search framework, the proposed algorithm enables efficient task allocation and collision avoidance while minimizing redundant flight paths. This approach significantly reduces overall energy consumption and ensures stable, highly coordinated autonomous flight among UAVs operating within the same inspection region. To support reliable data transmission, a hybrid communication framework that integrates 5G cellular networks with microwave communication links is adopted, enabling low-latency and real-time transmission of high-resolution video data from UAV platforms to edge and cloud servers. To further improve the accuracy of visual inspection, the YOLOv11 object detection model is enhanced by introducing an EMA attention mechanism. This optimization allows the network to better capture salient structural features of seawalls under varying illumination and background conditions. Based on this improvement, a customized YOLOv11-EMA target detection model tailored specifically for seawall inspection tasks is developed, enabling automated and robust identification of seawall structures and damage features from aerial video streams. In addition, a dedicated seawall erosion detection dataset is constructed to support model training and performance evaluation. Experimental results demonstrate that the proposed system achieves a detection accuracy of 90.1%, representing a 5.6% improvement over the original YOLOv11 model, with detection outcomes showing strong consistency with in situ monitoring data. Furthermore, the inspection efficiency of the UAV swarm is approximately five times higher than that of traditional manual inspections, substantially enhancing operational automation and data reliability. Overall, the proposed method provides effective technical support for the safe operation, real-time monitoring, and intelligent management of seawall infrastructure.