Super-resolution reconstruction of UAV maritime vessel target images
A method for compressive and blurry image reconstruction has been proposed to get rid of the conflict between real-time requirements and image clarity during the acquisition of maritime vessel images by unmanned aerial vehicles(UAVs).By utilizing an improved YOLOv8 detection model and Real-ESRGAN network,this method achieves real-time reconstruction of high-quality vessel images at the ground station under limited bandwidth and computational resource constraints with the steps of dataset construction,network training,debugging and deployment.Initially,the improved YOLOv8 model is used for precise detection and localization of vessel within the images.Subsequently,the Real-ESRGAN network is used to reconstruct the compressive and blurry images to restore high-resolution and details of the image.Experimental results indicate that the method enhances image clarity and detection accuracy significantly while greatly reducing bandwidth consumption,meeting the high real-time requirements of UAV-based vessel recognition,particularly in resource-constrained scenarios.This method provides an effective solution for UAVs in the field of maritime vessel monitoring,enhancing their capabilities for surveillance and identification,and laying the groundwork for the broader application of UAVs in marine monitoring.
万方数据
维普
