Underwater structures are crucial for national economic and social development. However, because of their complex environment, they are susceptible to damage during service. This damage should be prevented to minimize casualties and economic loss. Therefore, this study investigates the problems of disease identification and area statistics of underwater structures. To this end, the Dark-Retinex (DR) algorithm that can enhance the image of underwater structure defects is proposed. The algorithm consists of a combination of a dark channel algorithm and the Retinex algorithm. This study analyzes the current research status of underwater image processing technology, designs the overall framework of the DR algorithm, and uses the underwater structure disease image to verify the algorithm. Comparing the effect of the image with only the dark channel defogging and DR algorithm processing, the DR algorithm is observed to achieve “defogging” processing of underwater structural disease images to achieve better enhancement effects. Moreover, for accurate disease area statistics, the binary morphology and optimal threshold segmentation theories are combined to perform disease edge screening and remove interference information. Finally, accurate statistics of the proportion of diseased pixels are achieved, as well as the quantitative detection of surface diseases of underwater structures. After actual operational verification, the improved image dehazing and parallel boundary screening algorithms can achieve better application results to detect underwater structure defects and disease statistics. The objective evaluation shows that the DR algorithm facilitates image processing, can obtain relatively high-quality target images, and can solve the problems of time-consuming and labor-intensive detection of underwater structures, with significant risks and limitations. This helps pave the way for (1) the actual engineering of surface structure detection of underwater structures, (2) future storage in the database and assessment of hazard levels, and (3) a guide for engineering technicians to take corresponding maintenance measures.

中文翻译：

---

水下结构缺陷检测的改进暗通道去雾算法

水下结构对于国民经济和社会发展至关重要。但是，由于其复杂的环境，它们在使用过程中容易损坏。应防止这种损坏，以将人员伤亡和经济损失降至最低。因此，本研究调查了水下结构的疾病识别和面积统计问题。为此，提出了一种可以增强水下结构缺陷图像的Dark-Retinex（DR）算法。该算法由暗通道算法和Retinex算法组成。本研究分析了水下图像处理技术的研究现状，设计了DR算法的总体框架，并利用水下结构病害图像对算法进行了验证。将图像效果与仅暗通道除雾和DR算法处理进行比较，观察到DR算法可实现水下结构疾病图像的“除雾”处理，从而获得更好的增强效果。此外，为了进行准确的疾病面积统计，将二进制形态学和最佳阈值分割理论结合起来进行疾病边缘筛查并消除干扰信息。最后，获得了患病像素比例的准确统计信息，以及对水下结构表面疾病的定量检测。经过实际的操作验证，改进的图像去雾和平行边界筛选算法可以更好地应用于水下结构缺陷检测和疾病统计。客观评估表明，DR算法便于图像处理，可以获得相对高质量的目标图像，并且可以解决费时费力的水下结构检测问题，具有明显的风险和局限性。这有助于为（1）水下结构的表面结构检测的实际工程，（2）将来在数据库中存储和评估危害等级以及（3）工程技术人员采取相应维护措施的指南铺平道路。