Towards Automated Solar Panel Integrity: Hybrid Deep Feature Extraction for Advanced Surface Defect Identification

• URL: http://arxiv.org/abs/2604.10969v1
• Date: Mon, 13 Apr 2026 04:13:45 GMT
• Title: Towards Automated Solar Panel Integrity: Hybrid Deep Feature Extraction for Advanced Surface Defect Identification
• Abstract summary: We propose a novel hybrid method for defect detection in SOLAR plates by combining both handcrafted and deep learning features.<n> Experimental results evaluated on the augmented dataset show the superior performance, especially DenseNet-169 + Gabor (SVM)<n>In general, the proposed hybrid framework offers better defect-detection accuracy, resistance, and flexibility that has a solid basis on the real-life use of the automated PV panels monitoring system.
• Score: 0.9329978164030672
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To ensure energy efficiency and reliable operations, it is essential to monitor solar panels in generation plants to detect defects. It is quite labor-intensive, time consuming and costly to manually monitor large-scale solar plants and those installed in remote areas. Manual inspection may also be susceptible to human errors. Consequently, it is necessary to create an automated, intelligent defect-detection system, that ensures continuous monitoring, early fault detection, and maximum power generation. We proposed a novel hybrid method for defect detection in SOLAR plates by combining both handcrafted and deep learning features. Local Binary Pattern (LBP), Histogram of Gradients (HoG) and Gabor Filters were used for the extraction of handcrafted features. Deep features extracted by leveraging the use of DenseNet-169. Both handcrafted and deep features were concatenated and then fed to three distinct types of classifiers, including Support Vector Machines (SVM), Extreme Gradient Boost (XGBoost) and Light Gradient-Boosting Machine (LGBM). Experimental results evaluated on the augmented dataset show the superior performance, especially DenseNet-169 + Gabor (SVM), had the highest scores with 99.17% accuracy which was higher than all the other systems. In general, the proposed hybrid framework offers better defect-detection accuracy, resistance, and flexibility that has a solid basis on the real-life use of the automated PV panels monitoring system.

Related papers

• Real-Time Industrial Defect Detection on Edge Hardware Using Fine-Tuned YOLOv8: A Systematic Benchmark on the NEU Surface Defect Database and MVTec AD with Automotive & Battery Manufacturing Extensions [0.0]
  Industrial-YOLO is an edge-optimized framework built upon a fine-tuned YOLOv8 architecture for real-time industrial defect detection.<n>The proposed framework showcases highly robust, zero-velocity performance when deployed directly onto an active automotive assembly line.
  arXiv  Detail & Related papers  (2026-06-03T12:39:16Z)
• A novel YOLO26-MoE optimized by an LLM agent for insulator fault detection considering UAV images [2.2672499384619993]
  This paper proposes an optimized YOLO26-MoE, a novel object detection architecture that integrates a sparse Mixture-of-Experts (MoE) module into the high-resolution branch of the YOLO26 detector.<n>The proposed modification enables adaptive feature refinement for subtle and diverse fault patterns while preserving the efficiency of a one-stage detection framework.
  arXiv  Detail & Related papers  (2026-05-19T09:40:05Z)
• Amped: Adaptive Multi-stage Non-edge Pruning for Edge Detection [53.59823657644554]
  We propose an Adaptive Multi-stage non-edge Pruning framework for Edge Detection(Amped)<n>Amped identifies high-confidence non-edge tokens and removes them as early as possible to substantially reduce computation.<n>To mitigate the structural complexity of existing edge detection networks, we introduce a simple yet high-performance Transformer-based model, termed Streamline Edge Detector(SED)
  arXiv  Detail & Related papers  (2026-03-29T12:26:35Z)
• Method of UAV Inspection of Photovoltaic Modules Using Thermal and RGB Data Fusion [11.593007195936186]
  This study establishes a powerful new paradigm for proactive PV inspection.<n>The proposed system achieves a mean Average Precision (mAP@0.5) of 0.903 on the public PVF-10 benchmark.
  arXiv  Detail & Related papers  (2025-12-06T17:28:22Z)
• Vision-Based Object Detection for UAV Solar Panel Inspection Using an Enhanced Defects Dataset [0.0]
  This study presents a comprehensive evaluation of five state-of-the-art object detection models: YOLOv3, Faster R-CNN, RetinaNet, EfficientDet, and Swin Transformer.<n>A custom dataset, annotated in the COCO format and specifically designed for solar panel defect and contamination detection, was developed.<n>The results demonstrate the trade-offs between detection accuracy and computational efficiency, highlighting the relative strengths and limitations of each model.
  arXiv  Detail & Related papers  (2025-09-02T14:41:31Z)
• HOTSPOT-YOLO: A Lightweight Deep Learning Attention-Driven Model for Detecting Thermal Anomalies in Drone-Based Solar Photovoltaic Inspections [0.685316573653194]
  HotSPOT-YOLO is a lightweight artificial intelligence (AI) model that integrates an efficient convolutional neural network backbone and attention mechanisms to improve object detection.<n>This work highlights the integration of advanced AI techniques with practical engineering applications, revolutionizing automated fault detection in renewable energy systems.
  arXiv  Detail & Related papers  (2025-08-26T10:35:24Z)
• AI-Powered Machine Learning Approaches for Fault Diagnosis in Industrial Pumps [0.0]
  This study presents a practical approach for early fault detection in industrial pump systems using real-world sensor data.<n>The framework is scalable, interpretable, and suitable for real-time industrial deployment.
  arXiv  Detail & Related papers  (2025-08-21T13:33:09Z)
• Power Battery Detection [91.99787495748218]
  Power batteries are essential components in electric vehicles, where internal structural defects can pose serious safety risks.<n>We conduct a comprehensive study on power battery detection (PBD), which aims to localize the dense endpoints of cathode and anode plates from X-ray images for quality inspection.<n>We present PBD5K, the first large-scale benchmark for this task, consisting of 5,000 X-ray images from nine battery types with fine-grained annotations and eight types of real-world visual interference.
  arXiv  Detail & Related papers  (2025-08-11T09:35:25Z)
• AI-Powered Dynamic Fault Detection and Performance Assessment in Photovoltaic Systems [44.99833362998488]
  intermittent nature of photovoltaic (PV) solar energy leads to power losses of 10-70% and an average energy production decrease of 25%. Current fault detection strategies are costly and often yield unreliable results due to complex data signal profiles. This research presents a computational model using the PVlib library in Python, incorporating a dynamic loss quantification algorithm.
  arXiv  Detail & Related papers  (2024-08-19T23:52:06Z)
• A Self-Supervised Task for Fault Detection in Satellite Multivariate Time Series [45.31237646796715]
  This work proposes a novel approach leveraging Physics-Informed Real NVP neural networks, renowned for their ability to model complex and high-dimensional distributions.<n>The experiments involve various configurations, including pre-training with self-supervision, multi-task learning, and standalone self-supervised training.<n>Results indicate significant performance improvements across all settings.
  arXiv  Detail & Related papers  (2024-07-03T07:19:41Z)
• Unsupervised machine-learning shock-capturing technique for high-order solvers [0.0]
  We present a novel unsupervised machine learning shock capturing algorithm based on Gaussian Mixture Models (GMMs) The proposed GMM sensor demonstrates remarkable accuracy in detecting shocks and is robust across diverse test cases without the need for parameter tuning. Our study reveals the potential of unsupervised machine learning methods, exemplified by the GMM sensor, to improve the robustness and efficiency of advanced CFD codes.
  arXiv  Detail & Related papers  (2023-07-28T10:33:12Z)
• ASFD: Automatic and Scalable Face Detector [59.31799101216593]
  We propose to search an effective FAE architecture, termed AutoFAE, which outperforms all existing FAE modules in face detection with a considerable margin. In particular, our strong ASFD-D6 outperforms the best competitor with AP 96.7/96.2/92.1 on WIDER Face test, and the lightweight ASFD-D0 costs about 3.1 ms, more than 320 FPS.
  arXiv  Detail & Related papers  (2022-01-26T07:11:51Z)
• A Multi-Stage model based on YOLOv3 for defect detection in PV panels based on IR and Visible Imaging by Unmanned Aerial Vehicle [65.99880594435643]
  We propose a novel model to detect panel defects on aerial images captured by unmanned aerial vehicle. The model combines detections of panels and defects to refine its accuracy. The proposed model has been validated on two big PV plants in the south of Italy.
  arXiv  Detail & Related papers  (2021-11-23T08:04:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.