The methodology involves several key steps: first, using the YOLO11-OBB rotating object detection algorithm to accurately extract photovoltaic strings and ...
In conclusion, this research presents an effective solution for dust detection on solar panels by combining YOLO11 deep …
This study introduces an automated defect detection pipeline that leverages deep learning and computer vision to identif…
Dust accumulation significantly degrades the energy output of photovoltaic (PV) panels, particularly in arid and semi-ar…
At present, the main methods for detecting surface dust on solar photovoltaic panels include object detection, image seg…
As time passes, dust may form on the panels due to various weather conditions and environments where the panels are loca…
In recent years, solar energy has emerged as a pillar of sustainable development. However, maintaining panel efficiency …
In this paper, we propose a novel convolutional neural network architecture based on the EfficientNet framework, customi…
Dust pollution significantly reduces solar panel efficiency, while traditional detection methods are subjective and cost…
From a practical aspect, the created solution provides an automated, cost-effective, and simply deployed instrument for …
To this end, we utilize state-of-art deep learning-based image classification models and evaluate them on a publicly ava…
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