长江科学院院报2025，Vol.42Issue(9)：156-166,11.DOI:10.11988/ckyyb.20240836
基于偏振成像与深度学习的浑浊水体水下结构表观缺陷检测
Detection of Apparent Defects of Underwater Structures in Turbid Waters Based on Polarization Imaging and Deep Learning
摘要
Abstract
[Objective]In underwater engineering inspection,the turbid shallow water environment severely hin-ders the performance of machine vision-based methods for detecting surface defects in underwater structures.To ad-dress the challenge of defect detection in turbid water,this study proposes a lightweight three-stage underwater de-fect 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 polar-ization restoration model to analyze the polarization characteristics of light waves.This approach effectively sup-pressed 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 pro-cessing 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 net-work'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 po-larization restoration model exhibited superior performance in metrics such as image clarity and color fidelity com-pared 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 accu-racy 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(mAP0.5,mAP0 75,and mAP0.5-0.95).Compared to existing models,PCC-YOLOv7 significantly enhanced defect detection performance in turbid underwa-ter 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 techni-cal 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.关键词
浑浊水体/水下结构/缺陷检测/偏振成像/深度学习/超分辨率重建Key words
turbid water/underwater structure/defect detection/polarization imaging/deep learning/super-resolu-tion reconstruction分类
建筑与水利引用本文复制引用
吕宗桀,李俊杰,张学武..基于偏振成像与深度学习的浑浊水体水下结构表观缺陷检测[J].长江科学院院报,2025,42(9):156-166,11.基金项目
国家重点研发计划基金项目(2022YFB4703401) （2022YFB4703401）
