抄録
The demand for inspections has increased due to the aging of concrete structures and tile wall surfaces. The hammering test is a simple inspection method, but the inspector needs much experience distinguishing the hammering sounds. Therefore, we developed a device that automatically classifies the hammering sounds using deep learning. However, the hardware with GPU for deep learning is the one-board microcomputer, which has no display, a battery, or an input device, so the inspector cannot change the settings or check the status during the hammering test. Therefore, we used a smartphone instead of a one-board microcomputer. We also used a cloud GPU since a smartphone does not have GPU. The results showed that communication time was the bottleneck. So we considered using a 5G network and compared the classification time, training time, and battery life of the smartphone. As a result, although the training time remained the same, we found that the classification speed was 1.46 times faster than the conventional method, and the smartphone’s battery life was sufficient.
