关键词: 籽棉, 异性纤维, 深度学习, 人工智能, 图像处理

Abstract:

In order to detect foreign fibers in seed cotton with heavy shadows, seed cotton samples without cotton shell and leafs, and 12 types of foreign fibers with different colors, shapes and sizes were randomly distributed on a moving convey surface. And then, 520 seed cotton images were obtained under the illuminations of light emitting diode (LED) and 1 148 images were obtained under the illuminations of double light source of LED +  linear laser by a color line-scan camera. Then Faster RCNN deep-learning neural networks composed of 13 convolutional layers, 13 sampling layers and 4 pooling layers were constructed. After training, the neural networks were used for detecting foreign fibers in the two types of seed cotton images respectively. The experimental results indicated that the detecting rates of the targets in the images under the illumination of LED and LED + linear laser are 90.3% and 86.7%, respectively, by the Faster RCNN. Especially, the detecting rate of white color foreign fibers increase from 5.9% to 90.3%.

Key words: seed cotton, foreign fiber, deep-learning, artificial intelligence, image processing