Abstract
Detecting the direction of pile lay in carpet samples can cause a bottleneck in production of sample boards. This work develops and analyzes two automatic methods for detection in all three types of carpet: cut pile, loop pile, and patterned. One approach is visual, it employs two convolutional neural networks for classifying images of a carpet sample. The other is tactile, and uses a custom electromechanical device. It is shown that accuracy of either automatic system is on par with human operators and the systems can be used in tandem for improved results. These automated systems for detection of pile lay orientation should be able to improve production rates.
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