Joint detection of smoke and flame in Photovoltaic System based on deep learning

In recent years, the photovoltaic industry has developed vigorously where fire occurs frequently at photovoltaic power plants. This paper proposes an image defogging method based on parallel convolutional neural networks and a combined detection method for smoke and flame based on improved Faster R-CNN. Firstly, fire datasets used in the photovoltaic power plant scene are collected and produced, including two categories of smoke and flame. Then, one deep learning method is used to suppress the haze interference in the photovoltaic power plant scene. Finally, we use ResNet instead of VGG16 network, and integrate feature pyramid network(FPN) into regional candidate networks(RPN) to improve the original Faster R-CNN structure. A large number of experimental results show that our improved model has better effects on joint detection of smoke and flame.