Deep Spectral Siamese Network For Heterogeneous Object Verification In Amazon Robotic Warehouse

Automatic object verification is an important task in Amazon fulfillment centers where millions of shelves are filled with a wide assortment of packages of various sizes. In the current Amazon unstructured warehouse environment it is still cheaper to hire humans (even in the U.S.) than to develop customized robotic solutions. New advances in computer vision and deep learning can revolutionize Amazon robotic warehouse by developing new algorithms for learning to find objects by robots. The success of many deep learning algorithms depends on a large number of correct annotated data and high quality images. The images captured by Amazon robots are partially or completely occluded with plastic tapes and the content of some bins may not match the recorded inventory of that bin. Moreover, since most of the objects appear only once or twice in the entire dataset of incredibly large number of categories, it is difficult to achieve sufficient training for each class at a satisfactory level. To address these issues, we developed a novel deep spectral Siamese architecture for efficient verification with improved accuracy. Our proposed spectral Siamese network can accurately learn correlation between objects that have only a few examples for training in the noisy and blurry images. Experimental results on Amazon Bin Image demonstrate the effectiveness of the proposed framework both qualitatively and quantitatively.