Binocular Camera-Based Depth Recognition for Motion Monitoring and Response Analysis of Flexible Floating Structures for Offshore Photovoltaics

Abstract

Driven by the global goal of carbon neutrality, offshore floating photovoltaic (OFPV) technology has become a primary focus of photovoltaic research. In particular, flexible thin-film structures have become a central focus of research in sustainable energy development. It offers numerous advantages, including light weight, low cost, and strong adaptability to the marine environment. However, traditional experimental methods still face challenges in accurately capturing the motion response of flexible thin films. To address this issue, this study proposes a motion measurement and monitoring framework based on binocular vision. The framework is validated using gyroscope data, and the results demonstrate its high accuracy and real-time performance. The research team conducted experiments on a flexible floating photovoltaic structure in a wave flume, applying the proposed framework to monitor its motion response under wave excitation. The experimental results show that wave height and wave period have significant effects on the acceleration response of the thin film: higher wave heights lead to notably greater accelerations, whereas longer wave periods result in a gradual decrease in acceleration. Overall, the proposed framework provides reliable technical support for the design optimization and safety assessment of flexible thin-film FPV structures.

Keywords

Flexible floating photovoltaic structures / YOLOv8 / Binocular depth camera / Motion response

Author Contributions

Conceptualization, S.Y.; Methodology, S.Y. and X.Y.; Software, S.Y.; Validation, S.Y. and L.Y.; Formal Analysis, S.Y.; Investigation, S.Y.; Resources, J.L., X.Y., Y.Y. and C.L.; Data Curation, S.Y.; Writing—Original Draft Preparation, S.Y.; Writing—Review & Editing, X.Y.; Visualization, S.Y.; Supervision, J.L., Y.Y. and C.L.; Project Administration, J.L., X.Y. and Y.Y.; Funding Acquisition, J.L., X.Y. and Y.Y.

Ethics Statement

Not applicable. This study did not involve humans or animals.

Informed Consent Statement

Not applicable. This study did not involve human participants.

Data Availability Statement

The data presented in this study are available on reasonable request from the The data are not publicly available due to their large size and subsequent ongoing research.

Funding

This work was supported by the National Key R&D Program of China under Grant Number 2022YFB4200701, and National Natural Science Foundation of China under Grant Number 52571308, and Natural Science Foundation of Tianjin under Grant Number 24JCYBJC00870.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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