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Improved Dark Channel Defogging Algorithm for Defect Detection in Underwater Structures

机译:水下结构中缺陷检测的改进的暗通道缺失算法

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摘要

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.
机译:水下结构对于国家经济和社会发展至关重要。然而,由于其复杂的环境,它们在服务期间容易受到损坏。应防止这种损坏以最大限度地减少伤亡和经济损失。因此,本研究调查了水下结构的疾病鉴定和面积统计问题。为此,提出了可以增强水下结构缺陷的图像的黑暗视网膜(DR)算法。该算法包括暗信道算法和RetineX算法的组合。本研究分析了水下图像处理技术的当前研究现状,设计了DR算法的整体框架,并使用水下结构疾病图像来验证算法。将图像的效果与暗通道缺失的图像进行比较,观察到DR算法以实现水下结构疾病图像的“缺失”处理,以实现更好的增强效果。此外,对于准确的疾病区域统计,组合二进制形态和最佳阈值分割理论以进行疾病边缘筛选和去除干扰信息。最后,实现了患病像素比例的准确统计,以及水下结构的表面疾病的定量检测。在实际操作验证之后,改进的图像脱水和并行边界筛选算法可以实现更好的应用结果以检测水下结构缺陷和疾病统计。客观评估表明,DR算法有助于图像处理,可以获得相对高质量的目标图像,并且可以解决耗时和劳动密集型检测水下结构的问题,具有显着的风险和限制。这有助于为(1)水下结构的表面结构检测的实际工程,(2)在数据库中的未来存储和危险水平评估中,(3)工程技术人员指南采取相应的维护措施的指南。

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