CNN-based Deep Learning Approach for Micro-crack Detection of Solar Panels

Rahman, Md. Raqibur; Tabassum, Sanzana; Haque, Ehtashamul; Nishat, Mirza Muntasir; Faisal, Fahim; Hossain, Eklas

doi:10.1109/sti53101.2021.9732592

Cited by 43 publications

(13 citation statements)

References 21 publications

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“…Better training techniques, such as data augmentation, can also improve the model's accuracy. [68]. The method was to identify each microcrack individually, then aggregate the results using ensemble methods.…”

Section: Cnn-based Crack Detection Methodsmentioning

confidence: 99%

A Survey of CNN-Based Approaches for Crack Detection in Solar PV Modules: Current Trends and Future Directions

Hassan,

Dhimish

2023

Solar

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Detection of cracks in solar photovoltaic (PV) modules is crucial for optimal performance and long-term reliability. The development of convolutional neural networks (CNNs) has significantly improved crack detection, offering improved accuracy and efficiency over traditional methods. This paper presents a comprehensive review and comparative analysis of CNN-based approaches for crack detection in solar PV modules. The review discusses various CNN architectures, including custom-designed networks and pre-trained models, as well as data-augmentation techniques and ensemble learning methods. Additionally, challenges related to limited dataset sizes, generalizability across different solar panels, interpretability of CNN models, and real-time detection are discussed. The review also identifies opportunities for future research, such as the need for larger and more diverse datasets, model interpretability, and optimized computational speed. Overall, this paper serves as a valuable resource for researchers and practitioners interested in using CNNs for crack detection in solar PV modules.

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Section: Cnn-based Crack Detection Methodsmentioning

confidence: 99%

A Survey of CNN-Based Approaches for Crack Detection in Solar PV Modules: Current Trends and Future Directions

Hassan,

Dhimish

2023

Solar

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“…The Xception network architecture has been implemented for a variety of applications, including microcrack detection on solar panels from electroluminescence images [32], identification of medicinal plants from RGB images [33], and detection of faces created by a generative adversarial network [34]. Additionally, the Xception module has been implemented independently of the Xception network, such as in [35], which used the Xception module in a modified version of the U-Net CNN network architecture to extract buildings from high-resolution remote sensing images.…”

Section: Xceptionmentioning

confidence: 99%

Fuzzy-Based Image Contrast Enhancement for Wind Turbine Detection: A Case Study Using Visual Geometry Group Model 19, Xception, and Support Vector Machines

Ward,

Miller,

Engel

et al. 2024

Machines

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Traditionally, condition monitoring of wind turbines has been performed manually by certified rope teams. This method of inspection can be dangerous for the personnel involved, and the resulting downtime can be expensive. Wind turbine inspection can be performed using autonomous drones to achieve lower downtime, cost, and health risks. To enable autonomy, the field of drone path planning can be assisted by this research, namely machine learning that detects wind turbines present in aerial RGB images taken by the drone before performing the maneuvering for turbine inspection. For this task, the effectiveness of two deep learning architectures is evaluated in this paper both without and with a proposed fuzzy contrast enhancement (FCE) image preprocessing algorithm. Efforts are focused on two convolutional neural network (CNN) variants: VGG19 and Xception. A more traditional approach involving support vector machines (SVM) is also included to contrast a machine learning approach with our deep learning networks. The authors created a novel dataset of 4500 RGB images of size 210×210 to train and evaluate the performance of these networks on wind turbine detection. The dataset is captured in an environment mimicking that of a wind turbine farm, and consists of two classes of images: with and without a small-scale wind turbine (12V Primus Air Max) assembled at Utah Valley University. The images were used to describe in detail the analysis and implementation of the VGG19, Xception, and SVM algorithms using different optimization, model training, and hyperparameter tuning technologies. The performances of these three algorithms are compared in depth alongside those augmented using the proposed FCE image preprocessing technique.

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“…This crack might create inactive regions that do not contribute to power production. This dataset contains grayscale images with specialized cameras [38], [39]. • Satellite imagery: The global dataset for landcover classification is based on annual timeseries data from three different satellites: Sentinel-1, Sentinel-2, and Landsat-8.…”

Section: A Data Collectionmentioning

confidence: 99%

Optimized CNN Architectures Benchmarking in Hardware-Constrained Edge Devices in IoT Environments

Rosero-Montalvo,

Tözün,

Hernandez

2024

IEEE Internet Things J.

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Internet of Things (IoT) and Edge devices have grown in their application fields due to Machine learning (ML) models and their capacity to classify images into previously known labels, working close to the end-user. However, the model might be trained with several convolutional neural network (CNN) architectures that can affect its performance when developed in hardware-constrained environments, such as: Edge devices. In addition, new training trends suggest using transfer learning techniques to get an excellent feature extractor obtained from one domain and use it in a new domain, which has not enough images to train the whole model. In light of these trends, this work benchmarks the most representative CNN architectures on emerging Edge devices, some of which have hardware accelerators. The ML models were trained and optimized using a small set of images obtained in IoT environments and using transfer learning. Our results show that unfreezing until the last 20 layers of the model's architecture can be fine-tuned correctly to the new set of IoT images depending on the CNN architecture. Additionally, quantization is a suitable optimization technique to shrink 2x or 3x times the model leading to a lighter memory footprint, lower execution time, and battery consumption. Finally, the Coral Dev Board can boost 100x the inference process, and the EfficientNet model architecture keeps the same classification accuracy even when the model is adopted to a hardware-constrained environment.

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CNN-based Deep Learning Approach for Micro-crack Detection of Solar Panels

Cited by 43 publications

References 21 publications

A Survey of CNN-Based Approaches for Crack Detection in Solar PV Modules: Current Trends and Future Directions

A Survey of CNN-Based Approaches for Crack Detection in Solar PV Modules: Current Trends and Future Directions

Fuzzy-Based Image Contrast Enhancement for Wind Turbine Detection: A Case Study Using Visual Geometry Group Model 19, Xception, and Support Vector Machines

Optimized CNN Architectures Benchmarking in Hardware-Constrained Edge Devices in IoT Environments

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