Imagen de Portada

Hybrid Regularized Variational Minimization Method to Promote Visual Perception for Intelligent Surface Vehicles Under Hazy Weather Condition

Guardado en:
Publicado en:Journal of Marine Science and Engineering vol. 13, no. 10 (2025), p. 1991-2011
Autor principal: Li Peizheng
Otros Autores: Qiao Dayong, Luo Caofei, Wan Desong, Li, Guilian
Publicado:
MDPI AG
Materias:
Visual perception
Deep learning
Algorithms
Surface vehicles
Weather
Autonomous surface vehicles
Unmanned vehicles
Computer vision
Maps
Numerical analysis
Neural networks
Optimization
Navigational safety
Regularization methods
Image quality
Perception
Object recognition
Navigation safety
Vehicles
Parameter estimation
Environmental
Acceso en línea:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen:Intelligent surface vehicles, including unmanned surface vehicles (USVs) and autonomous surface vehicles (ASVs), have gained significant attention from both academic and industrial communities. However, shipboard maritime images captured under hazy weather conditions inevitably suffer from a blurred, distorted appearance. Low-quality maritime images can lead to negative effects on high-level computer vision tasks, such as object detection, recognition and tracking, etc. To avoid the negative influence of low-quality maritime images, it is necessary to develop a visual perception enhancement method for intelligent surface vehicles. To generate satisfactory haze-free maritime images, we propose development of a novel transmission map estimation and refinement framework. In this work, the coarse transmission map is obtained by the weighted fusion of transmission maps generated by dark channel prior (DCP)- and luminance-based estimation methods. To refine the transmission map, we take the segmented smooth feature of the transmission map into account. A joint variational framework with total generalized variation (TGV) and relative total variation (RTV) regularizers is accordingly proposed. The joint variational framework is effectively solved by an alternating-direction numerical algorithm, which decomposes the original nonconvex nonsmooth optimization problem into several subproblems. Each subproblem could be efficiently and easily handled using the existing optimization algorithm. Finally, comprehensive experiments are conducted on synthetic and realistic maritime images. The imaging results have illustrated that our method can outperform or achieve comparable results with other competing dehazing methods. The promoted visual perception is beneficial to improve navigation safety for intelligent surface vehicles under hazy weather conditions.
ISSN:2077-1312
DOI:10.3390/jmse13101991
Fuente:Engineering Database