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

arXiv: https://arxiv.org/abs/2604.10969 · HTML: https://mlsanity.com/paper/2604.10969/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 49 references that are in the map.
- First posted on arXiv 13 Apr 2026.

## Builds on (most cited first, 4 of 4)

- [Artificial Intelligence Based Prognostic Maintenance of Renewable Energy Systems: A Review of Techniques, Challenges, and Future Research Directions](https://mlsanity.com/paper/2104.12561/) (2021)
- [Exploring Convolutional Neural Networks for Rice Grain Classification: An Explainable AI Approach](https://mlsanity.com/paper/2505.05513/) (2025)
- [Crowd Scene Analysis using Deep Learning Techniques](https://mlsanity.com/paper/2505.08834/) (2025)
- [Local Binary Pattern(LBP) Optimization for Feature Extraction](https://mlsanity.com/paper/2407.18665/) (2024)

## Built on by (most cited first, 2 of 2)

- [Efficient Adaptation of LLMs for Hate Speech Detection in Low-Resource Languages: A Comparative Study on Roman Urdu](https://mlsanity.com/paper/2608.18142/) (2026)
- [Integrating Triaxial IMU Sensors and Ensemble Learning for Effective Parkinson Disease Severity Classification](https://mlsanity.com/paper/2608.28602/) (2026)

## Abstract (arXiv)

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.