1 research outputs found
Life detection strategy based on infrared vision and ultra-wideband radar data fusion
The life detection method based on a single type of information source cannot
meet the requirement of post-earthquake rescue due to its limitations in
different scenes and bad robustness in life detection. This paper proposes a
method based on deep neural network for multi-sensor decision-level fusion
which concludes Convolutional Neural Network and Long Short Term Memory neural
network (CNN+LSTM). Firstly, we calculate the value of the life detection
probability of each sensor with various methods in the same scene
simultaneously, which will be gathered to make samples for inputs of the deep
neural network. Then we use Convolutional Neural Network (CNN) to extract the
distribution characteristics of the spatial domain from inputs which is the
two-channel combination of the probability values and the smoothing probability
values of each life detection sensor respectively. Furthermore, the sequence
time relationship of the outputs from the last layers will be analyzed with
Long Short Term Memory (LSTM) layers, then we concatenate the results from
three branches of LSTM layers. Finally, two sets of LSTM neural networks that
is different from the previous layers are used to integrate the three branches
of the features, and the results of the two classifications are output using
the fully connected network with Binary Cross Entropy (BEC) loss function.
Therefore, the classification results of the life detection can be concluded
accurately with the proposed algorithm.Comment: 6 pages, 7 figures, conferenc