LaserNet: a method of laser stripe center extraction under non-ideal conditions.

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Abstract

The extraction of the center of a laser stripe is a key step in line-structure measurement, where noise interference and changes in the surface color of an object are the main factors affecting extraction accuracy. To obtain sub-pixel level center coordinates under such non-ideal conditions, we propose LaserNet, a novel deep learning-based algorithm, to the best of our knowledge, which consists of a laser region detection sub-network and a laser position optimization sub-network. The laser region detection sub-network is used to determine potential stripe regions, and the laser position optimization sub-network uses the local image of these regions to obtain the accurate center position of the laser stripe. The experimental results show that LaserNet can eliminate noise interference, handle color changes, and give accurate results under non-ideal conditions. The three-dimensional reconstruction experiments further demonstrate the effectiveness of the proposed method.