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

2026-04-13

AI Summary

This study addresses the high cost, low efficiency, and limited scalability of manual inspection for photovoltaic (PV) panel defects, particularly in large-scale or remote solar farms. To overcome these challenges, the authors propose a hybrid defect detection framework that integrates handcrafted features—specifically Local Binary Patterns (LBP), Histogram of Oriented Gradients (HoG), and Gabor features—with deep features extracted from DenseNet-169. The framework further enhances robustness and generalization by fusing predictions from multiple classifiers, including Support Vector Machine (SVM), XGBoost, and LightGBM (LGBM). Evaluated on an augmented dataset, the proposed method achieves a state-of-the-art accuracy of 99.17% using the DenseNet-169 + Gabor + SVM configuration, significantly outperforming existing approaches and demonstrating its practical effectiveness and innovation in real-world PV monitoring applications.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• solar panel defect detection
• automated inspection
• surface defect identification
• photovoltaic monitoring
• intelligent defect-detection system

Innovation

• hybrid feature extraction
• deep learning
• handcrafted features
• solar panel defect detection
• DenseNet-169