From Event: SPIE Commercial + Scientific Sensing and Imaging, 2018
A convolutional neural network (CNN) was developed to recognize sprinkler activation based on long-wave infrared (LWIR) images, creating a nonintrusive, real-time model for detecting sprinkler activation. Training data were taken from ten large-scale fire tests with storage heights ranging between 4.6 m and 13.7 m and ceiling heights ranging between 6.1 m and 15.2 m. A sample of 25,000 LWIR images was extracted from the fire tests, split 70/30 between training/testing data. To prevent overfitting, the images were randomly reversed and cropped. The time required to train the model was reduced by 96% through GPU computing. The overall accuracy of the model was 99.7% for both pendent and upright sprinklers. The methodology described in this study can be generalized and applied to other image classification problems.
J. de Vries and Stanislav Kostka, "Building a brain: how convolution neural networks can predict sprinkler activations," Proc. SPIE 10661, Thermosense: Thermal Infrared Applications XL, 106610E (Presented at SPIE Commercial + Scientific Sensing and Imaging: April 17, 2018; Published: 14 May 2018); https://doi.org/10.1117/12.2310130.
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