Photovoltaic Hot Spot Recognition Algorithm Based on Improved ResNet50 Network
To improve the recognition accuracy of samples under uneven distribution, an improved ResNet50 convolutional neural network photovoltaic hot spot recognition algorithm is proposed. First, in order to increase the initial infrared texture information inflow and adjust the network width, a head grouping feature extraction module is designed and embedded into the residual network to improve the network' s extraction capability of image subtle features;Then, by combining channel attention mechanism with residual module, the weight of hot spot feature information between network channels is increased to improve model recognition performance and network con-vergence speed;Last, through data preprocessing methods such as image conversion into HSV color space and average H-component gradient histogram peak, negative samples are converted into multi classification datasets and used in the hot spot recognition network model to achieve visualization of the hot spot recognition results. Results show that compared to other algorithms,improved ResNet50 net-work significantly improves detection accuracy.
photovoltaic hot spotimage recognitionHSV color spaceResNet50