Research on Fault Identification of Ship PV System Based on Aerial Photography Image Recognition

Ma, Ziyan; Zhang, Yan; Li, Huiyuan

doi:10.1109/ictis.2019.8883574

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2022

2024

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Cited by 2 publications

(2 citation statements)

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“…The fault identification of marine PV systems is extremely difficult due to the long-term exposure to the marine environment and an aerial image recognition fault identification system is designed in [34]. The article [35] outlines the key factors that impact ship stability when loading and unloading heavy cargo.…”

Section: Introductionmentioning

confidence: 99%

Leveraging an installed standalone photovoltaic system for eco-friendly shipping

Paulson,

Babu,

Chacko

2024

Eng. rev. (Online)

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Section: Introductionmentioning

confidence: 99%

Leveraging an installed standalone photovoltaic system for eco-friendly shipping

Paulson,

Babu,

Chacko

2024

Eng. rev. (Online)

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“…Driven by economic globalization, trade, and communication between countries have become more frequent, the requirements for the transportation of goods becoming higher and higher [1], [2]. Since the large size, low cost, and inconvenient transportation, ship waterway transportation has irreplaceable importance in the field of transportation.…”

Section: Introductionmentioning

confidence: 99%

Denoising of Maritime Unmanned Aerial Vehicles Photography Based on Guidance Correlation Pixel Sampling and Aggregation

2022

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In this paper, we proposed a guidance correlation pixel Sampling and aggregation image denoising for maritime Unmanned Aerial Vehicles(UAVs) photography, providing a reliable data base for maritime reconnaissance work. The overall step is mainly composed of two parts, pixel sampling and pixel aggregation. To improve the content correlation of sampled pixels, we propose a guided sampling scheme based on the basic estimated map and extend this algorithm to the restoration of maritime UAVs image. Finally, a UAVs image denoising system is shown. Our experimental results show that the proposed algorithm can effectively remove noise and achieves 32.98dB and 31.83dB in Set12 and BSD68 datasets with less and image distortion. In the actual scene, the PSNR of our denoising algorithm has reached 35.33dB, meets the basic needs of practical vision and follow-up research.

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Research on Fault Identification of Ship PV System Based on Aerial Photography Image Recognition

Cited by 2 publications

References 7 publications

Leveraging an installed standalone photovoltaic system for eco-friendly shipping

Leveraging an installed standalone photovoltaic system for eco-friendly shipping

Denoising of Maritime Unmanned Aerial Vehicles Photography Based on Guidance Correlation Pixel Sampling and Aggregation

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