基于YOLOv3深度学习的海雾气象条件下海上船只实时检测

海雾气象条件下船只高精度检测识别面临较大困难，传统的目标识别、定位方法效果差强人意。作者围绕海雾气象条件下不同类型船只的实时检测问题，提出一种基于YOLOv3深度学习的实时海上船只检测新思路。首先构建清晰图片和模糊图片（海雾、雨）的判别方法，实现图片清晰度分类处理；其次为提高海雾气象条件下海上船只的实时检测精度，消除海雾遮挡对目标识别的影响，运用暗通道先验去雾方法对含有海雾的图像实行去雾；最后基于YOLOv3深度学习算法对精细处理后的图像进行船只实时检测。实验结果表明该方法能够在海雾气象条件下高效、准确地检测到船只，对海上复杂环境条件下的船只实时检测研究具有一定的理论指导意义。

Real-time detection of marine vessels under sea fog weather conditions based on YOLOv3 deep learning

High-precision detection and recognition of vessels under sea fog weather conditions encounter numerous difficulties, and traditional target recognition methods rarely achieve satisfactory results. This study focuses on detection of different types of vessels under complex sea fog weather condition and proposes a new real-time marine vessel detection method based on YOLOv3 deep learning. First, a discriminative method for clear and fuzzy pictures (e.g., sea fog and rain) is constructed to realize the classification and processing of picture sharpness. Then, to improve the detection precision of marine vessels under complex sea fog weather condition, the dark channel prior dehazing algorithm is used to suppress the effect of sea fog occlusion on target recognition. Finally, real-time detection of vessels is performed on the finely processed images on the basis of the YOLOv3 deep learning algorithm. The experimental results show that the proposed method can be used to efficiently and accurately detect vessels under sea fog weather condition and has certain theoretical guidance significance for research on target vessel recognition under complex marine conditions.