浅水环境常呈现浑浊特征,导致光学图像出现模糊、色偏、对比度低等问题。浑浊水体中散射粒子会遮蔽水下结构表观缺陷信息,造成缺陷识别率低、检测效率低和分类不准等问题。针对这些挑战,提出一种基于偏振成像和深度学习的轻量级三阶段水下缺陷检测方法,借助偏振复原模型、超分辨率重建模型和缺陷检测模型3个子模型实现缺陷检测。偏振复原模型用于实现浑浊水体的清晰成像和水下缺陷图像复原,CAA-SRGAN超分辨率重建模型用于获取高分辨率水下缺陷图像,CBAM-YOLOv7缺陷检测模型用于检测水下结构常见的裂缝、孔洞和剥落缺陷,最终形成适用于浑浊水体水下结构的PCC-YOLOv7缺陷检测模型。分别通过与现有先进图像复原、超分辨率重建和目标检测方法进行对比分析,结果显示3个子模型的输出结果在各自评价指标中均有提升。PCC-YOLOv7缺陷检测模型对平均精确度指标(mAP_0.5、mAP_0.75、mAP_0.5~0.95)的提升幅度均值达33.5%。本文所构建的模型相较于现有模型,对浑浊水下检测场景有着更强的适配性,能够为浑浊水体中水下结构表观缺陷检测工作提供切实可行的方法。

[Objective] In underwater engineering inspection, the turbid shallow water environment severely hinders the performance of machine vision-based methods for detecting surface defects in underwater structures. To address the challenge of defect detection in turbid water, this study proposes a lightweight three-stage underwater defect detection method that integrates polarization imaging and deep learning techniques. A defect detection model, named PCC-YOLOv7, is developed. [Methods] First, polarization imaging technology was combined with a polarization restoration model to analyze the polarization characteristics of light waves. This approach effectively suppressed scattering interference in turbid water, thereby achieving clear imaging of turbid environments and restoring defect images. Consequently, defect details obscured by scattering particles were reconstructed. Second, the CAA-SRGAN (Coordinate Attention ACON-Super Resolution Generative Adversarial Network) model was introduced. By employing an improved attention mechanism and a generative adversarial network structure, super-resolution processing was performed on the restored images. This yielded high-resolution underwater defect images, providing a high-quality data foundation for subsequent precise detection. Finally, a defect detection model based on CBAM-YOLOv7 was established, where the convolutional block attention module (CBAM) was utilized to enhance the network’s focus on defect features. Leveraging the advanced YOLOv7 object detection framework, common underwater structural defects, including cracks, holes, and spalling can be rapidly and accurately identified. These three sub-models worked collaboratively to form a comprehensive detection system. [Results] For image restoration, the polarization restoration model exhibited superior performance in metrics such as image clarity and color fidelity compared to current restoration methods. The CAA-SRGAN model generated images with notable improvements in detail texture preservation and resolution enhancement. The CBAM-YOLOv7 defect detection model achieved higher accuracy in both defect localization and classification. A comprehensive evaluation of the PCC-YOLOv7 defect detection model revealed an average improvement of 33.5% in mean average precision (mAP_0.5, mAP_0.75, and mAP_0.5-0.95). Compared to existing models, PCC-YOLOv7 significantly enhanced defect detection performance in turbid underwater environments, effectively improving both recognition rate and detection efficiency. [Conclusions] The PCC-YOLOv7 defect detection model innovatively integrates polarization imaging technology with deep learning. Through the collaborative operation of three functionally complementary sub-models, it successfully addresses the challenge of detecting surface defects in underwater structures in turbid water. Compared to existing models, the proposed model demonstrates enhanced adaptability to turbid underwater detection scenarios. It enables stable and efficient detection of surface defects in underwater structures under complex turbid conditions, providing a practical technical solution for the safety assessment and maintenance of underwater structures. Future work may focus on further optimizing the model structure and extending its application to more underwater scenarios.