Fast object detection of anomaly photovoltaic (PV) cells using deep neural networks

Anomaly detection in photovoltaic (PV) cells is crucial for ensuring the efficient operation of solar power systems and preventing potential energy losses. In this paper, we propose an enhanced YOLOv7-based deep learning framework for fast and accurate anomaly detection in PV cells. Our approach incorporates Partial Convolution, Switchable Atrous Convolution and novel data augmentation techniques to address the challenges of varying defect sizes, complex backgrounds. The Partial Convolution component effectively manages the irregularities in PV cell images, reducing false detections. On the other hand, the Switchable Atrous Convolution enhances the model’s adaptability to different defect scales and spatial resolutions, leading to improved localization and classification performance. We evaluate our model on a large-scale dataset of PV cell images, demonstrating its superiority over existing methods in terms of detection accuracy, speed, and robustness. Our proposed framework offers a practical and reliable solution for real-time anomaly detection in PV cells, facilitating timely maintenance and maximizing the performance of solar energy systems. The integration of these advanced convolution techniques into the YOLOv7 model not only improves detection capabilities but also paves the way for further research and development in the field of deep learning-based anomaly detection.