Double-stream Convolutional Neural Networks for Machine Vision Inspection of Natural Products

There are known applications of convolutional neural networks to vision inspection of natural products. For many products it is sufficient to acquire and process a single image, but some might require imaging from two sides. Human experts performing quality inspection of malting barley typically only observe one side of each grain, but in doubtful cases look at both sides, intrinsically combining the information. In this paper, we make two contributions. We present a method for determining whether imaging objects from two sides yields performance benefits over single-sided imaging. Then we introduce a double-stream convolutional network for reasoning from two images simultaneously and analyze several methods of combining information from two streams. We find that when orientation of the object is unpredictable and the streams are not specialized to process a particular view, a fully shared architecture combining information on the prediction level yields best performance (98.7% accuracy on our dataset).