ENHANCING THE CLARITY OF BLURRY DRONE IMAGES OF VESSELS AT SEA

To address the trade-off between real-time performance and image clarity in UAV-based maritime ship target acquisition, this paper proposes an image compression and blur reconstruction method. The approach leverages an improved YOLOv8 detection model and the Real-ESRGAN network, incorporating dataset construction, network training and tuning, and deployment to achieve real-time reconstruction of high-quality ship target images on the ground under limited bandwidth and computational resources. First, the improved YOLOv8 model is employed for precise detection and localization of ship targets in the images. Then, the Real-ESRGAN network is utilized to reconstruct compressed and blurry images, restoring high-resolution details. Experimental results demonstrate that the proposed method significantly enhances image clarity and detection accuracy while substantially reducing bandwidth consumption. It meets the high real-time requirements of UAV-based ship recognition and performs exceptionally well in resource-constrained environments. This work provides an effective solution for UAV-based maritime ship monitoring, improving UAV surveillance and recognition capabilities and laying a foundation for the broader application of UAVs in ocean monitoring.