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Pruning filters for efficient convolutional neural networks for image recognition of environmental hazards
Pruning filters for efficient convolutional neural networks for image recognition of environmental hazards
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机译:用于有效卷积神经网络的修剪过滤器,用于环境危害的图像识别
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摘要
Systems and methods for predicting changes to an environment, including a plurality of remote sensors, each remote sensor being configured to capture images of an environment. A processing device is included on each remote sensor, the processing device configured to recognize and predict a change to the environment using a pruned convolutional neural network (CNN) stored on the processing device, the pruned CNN being trained to recognize features in the environment by training a CNN with a dataset and removing filters from layers of the CNN that are below a significance threshold for image recognition to produce the pruned CNN. A transmitter is configured to transmit the recognized and predicted change to a notification device such that an operator is alerted to the change.
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