Abstract:Recycling and sorting nonferrous metals from scrap machinery is of great significance for environmental protection and resource conservation. This paper proposes a visual recognition method for nonferrous metal crushing materials based on deep residual networks, so as to make the sorting process automatic. In order to obtain the regions of interest (ROIs) of the metal crushing materials from the images acquired by the color CCD camera, Otsu method is improved through inverse-distance weighting interpolation algorithm, so that the threshold of image segmentation can be adaptively adjusted for better segmenting result. The ROIs of nonferrous metal crushed aggregates as the input signals of a 39-layer deep residual networks are used to train the networks. The deep residual networks output the categories of each nonferrous metal crushed aggregate. The industrial computer controls the sorting mechanism according to the positions and categories of each nonferrous metal crushed aggregate on the conveyor belt through this algorithm and camera calibration. For comparison, this paper carried out the recognition test of nonferrous metal crushed aggregate based on the traditional convolutional networks, such as AlexNet, VGGNet16, VGGNet39, GoogLeNet (Inception V2). Experiments showed that the recognition accuracies of the method based on deep residual networks for aluminum, copper and other materials are respectively 98.7%, 98.9% and 96.2%, which are superior to those based on the traditional convolutional networks.