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Visual sorting of recyclable goods using a support vector machine

Mounting environmental concerns and changing attitudes have led to recycling programs to divert waste from entering landfill sites. This trend has led municipalities to explore improved methods and tools such as machine vision for sorting and managing the growing volume of recyclable materials. This paper describes an approach to visual sorting using image intensity data and a support vector machine applied to the unique problem of sorting polycoat containers from plastic bottles. The approach is rotation, translation and scale invariant since it uses features derived from image histograms. We also demonstrate that the approach is robust to the size, shape, varied labeling and deformation of the recycled material. An experiment is performed to verify the approach using separate test and training data. Despite the use of a modest number of training images, the system achieves a classification accuracy of over 96% using images obtained from a single grey-scale camera.