由于光线在水中传播会受到介质的吸收和散射作用，在水下对油套管表面进行腐蚀缺陷的可视化监测时，图像会出现退化现象，影响后续缺陷特征的识别。针对上述问题，提出一种基于改进暗通道先验模型的水下油套管表面腐蚀缺陷特征的增强方法，首先针对弯曲的油套管表面产生的光照不均匀以及图像过曝的问题，根据局部区域内的像素变化完成图像的光照校正。然后针对均匀化计算后引入的特征减弱问题，根据局部高频特征，完成图像的细节增强。进一步，利用暗通道先验模型，消除浑浊液体对图像的干扰，从而实现腐蚀缺陷的可视化特征的增强。实验结果表明：该方式在一定程度上还原了水下图像的色彩，提升了图像清晰度，强化了可视化特征。使用YOLOv5对缺陷进行识别，使用所提算法处理的图像识别率有显著提升。

Due to the absorption and scattering effects of the medium, the propagation of light underwater leads to the degradation of images captured for visual monitoring of corrosion defects on the surface of underwater oil casings, which affects the subsequent identification of defect features. To address this issue, an enhanced method for visualizing corrosion defect features on the surface of underwater oil casings was proposed, based on an improved dark channel prior model. First, to address the problems of uneven illumination and overexposure caused by the curved surface of the oil casing, illumination correction was performed based on pixel variations within local regions. Then, to mitigate the weakening of features introduced by homogenization calculations, detail enhancement was achieved through local high-frequency features. Furthermore, the dark channel prior model was applied to eliminate the interference of turbid liquids on the image, thereby enhancing the visualization of corrosion defect features. Experimental results indicate that this method partially restores the color of underwater images, improves image clarity, and enhances visual features. By utilizing YOLOv5 for defect recognition, the recognition rate of images processed with the proposed algorithm is significantly improved.