🤖 AI Summary
This study addresses the lack of physically interpretable linkage between existing image dehazing quality metrics and actual visibility distance, which hinders their utility in maritime navigation safety decisions. To bridge this gap, the authors construct a Maritime Simulated Visibility Dataset (MSVD) using Unity3D and propose a visibility-oriented evaluation framework that leverages object detection accuracy as an intermediary proxy. This framework uniquely maps dehazing performance directly to quantifiable gains in visual range, establishing a novel visibility metric with both physical interpretability and operational safety relevance. The approach is validated across diverse imaging conditions, demonstrating consistent reliability. Notably, MSVD serves as the first maritime simulation benchmark with precisely annotated visibility distances, significantly enhancing the quantitative assessment of dehazing algorithms in terms of navigational safety and operational efficiency.
📝 Abstract
Visibility distance is critical to maritime navigational safety because it determines the effective observation range of shipborne and shore-based monitoring systems. Under hazy conditions, degraded visual information shortens observable distance and increases navigational risks and economic losses. Although numerous image dehazing methods have been developed, conventional image quality assessment metrics, such as PSNR, SSIM, FSIM, FADE, and NIQE, cannot establish a physically interpretable relationship between restoration quality and practical visibility thresholds. To address this limitation, this work proposes a visibility-oriented evaluation framework that links dehazing performance with visible-distance estimation. First, a Maritime Simulated Visibility Dataset (MSVD) is constructed using Unity3D to simulate maritime traffic scenes under graded visibility conditions. The dataset provides paired hazy and clear images with precise visibility annotations, enabling quantitative analysis of visibility restoration. Second, a dehazing visibility evaluation metric is developed by using object detection accuracy as an intermediate indicator. By establishing a mapping between visibility distance and detection performance, the proposed metric converts image restoration improvements into measurable visibility gains. Six representative dehazing methods are evaluated using both conventional image quality metrics and the proposed visibility metric. Experimental results under different imaging conditions demonstrate that MSVD provides a reliable benchmark for evaluating dehazing performance across graded visibility levels, while the proposed metric enables interpretable and reliable visible-distance estimation, thereby supporting the assessment of navigational safety and operational efficiency.