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Aerial Imaging-Based Soiling Detection System for Solar Photovoltaic Panel Cleanliness Inspection.

Umair Naeem1, Ken Chadda2, Sara Vahaji1

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|February 13, 2025
PubMed
Summary
This summary is machine-generated.

An AI model called SDS-YOLO, using Unmanned Aerial Vehicles (UAVs), accurately detects soiling on solar panels, including challenging bird droppings. This improves solar energy monitoring and efficiency.

Keywords:
PV inspectionaerial imagingobject detectionsoiling detection

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Area of Science:

  • Renewable Energy
  • Artificial Intelligence
  • Computer Vision

Background:

  • Solar photovoltaic (PV) panel inspection relies on Unmanned Aerial Vehicles (UAVs) with visual cameras for monitoring.
  • Soiling, especially bird droppings, significantly reduces solar panel power generation and can cause hotspots.
  • Detecting small, indistinct soiling like bird droppings from aerial images presents a significant challenge.

Purpose of the Study:

  • To develop an AI-assisted soiling detection methodology for solar PV panels using UAV-captured RGB images.
  • To create an autonomous, end-to-end soiling detection model capable of identifying common soiling types like dust and bird droppings.
  • To address the specific challenges of detecting small and indistinct bird droppings in aerial imagery.

Main Methods:

  • A custom AI model, SDS-YOLO (Soiling Detection System YOLO), was developed, incorporating a Convolutional Block Attention Module (CBAM).
  • The model features two dedicated detection heads optimized for distinguishing between dust and bird droppings.
  • A dataset of aerial RGB images featuring PV panels with dust and bird droppings was collected for training and validation.

Main Results:

  • SDS-YOLO demonstrated significantly improved detection accuracy for bird droppings compared to existing YOLO models (v5, v8, v11).
  • Integration of CBAM led to a 40.2% increase in mAP50 and a 26.6% F1 score improvement for bird dropping detection.
  • The model also showed robust performance for dust detection, with improved feature extraction and reduced false positives due to CBAM.
  • SDS-YOLO achieved a 24% reduction in parameter count, enhancing its suitability for edge computing.

Conclusions:

  • The proposed SDS-YOLO model effectively detects various soiling types on solar panels, particularly excelling at identifying challenging bird droppings.
  • The CBAM integration is crucial for enhancing feature extraction and improving detection accuracy, especially for small and indistinct objects.
  • SDS-YOLO offers an efficient and accurate solution for solar panel inspection and monitoring, suitable for deployment on edge devices.