Title: Real-time convolutional networks for sonar image classification in low-power embedded systems,
Abstract: Deep Neural Networks have impressive classification performance, but this
comes at the expense of significant computational resources at inference time.
Autonomous Underwater Vehicles use low-power embedded systems for sonar image
perception, and cannot execute large neural networks in real-time. We propose
the use of max-pooling aggressively, and we demonstrate it with a Fire-based
module and a new Tiny module that includes max-pooling in each module. By
stacking them we build networks that achieve the same accuracy as bigger ones,
while reducing the number of parameters and considerably increasing
computational performance. Our networks can classify a 96x96 sonar image with
98.8 - 99.7 accuracy on only 41 to 61 milliseconds on a Raspberry Pi 2, which
corresponds to speedups of 28.6 - 19.7.