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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.

The objective of this review paper is to provide an overview of the current state-of-the-art in solar road deployment, including the availability of anti-reflection and anti-soiling coating materials for photovoltaic (PV) technology. Solar roads are built using embedded PV panels that convert sunlight into electricity, which can be stored for later use. Prototypes of solar roads have been tested on various continents, but the lack of suitable PV materials has limited their effectiveness compared to conventional PV systems. By analyzing the existing literature on solar roads and PV materials, including anti-reflection and anti-soiling coatings, we aim to identify gaps in knowledge and propose an action plan to improve the resiliency, durability, and reliability of PV panels in solar road applications. This will enable the deployment of solar roads as a clean, renewable energy source.