Traditional commodity recognition processes generally use the more classic image recognition and machine learning algorithms such as support vector machines(SVM), random forest or AdaBoost, then use the basic characteristics of the gradient, texture or color of the target image to recognize commodities. It can be applied in a relatively simple background, but it is hard to have a more prominent performance in a complicated background environment, and it is difficult to achieve a high accuracy. At present, the convolution neural network(CNN), which is superior in target recognition, has become the first choice in many target recognition scenarios. Considering the hardware configuration cost of service robot, Faster R-CNN, a fast algorithm of region-based convolutional neural network(R-CNN), was introduced into the system and identified by CPU. The CNN network was used to extract image features and access to a regional proposal layer behind it. The experimental results show that it is feasible to apply the deep learning recognition method to the service robot platform. The recognition effect is accurate and the test results are good.

[2019-10-05] Printed Article /doc/2018.03.0311/v2