NEC's Object Recognition Technologies and their Industrial Applications

Recent advancements in image recognition technologies has enabled image recognition-based systems to be widely used in real world applications. In this talk, I will introduce NEC's image-based object recognition technologies targeted for recognizing various manufactured goods and retail products from a camera, and talk about their industrial applications which we have developed and commercialized. These image-based object recognition technologies enable highly efficient and cost-effective management of goods and products throughout their life-cycle (manufacturing, distribution, retail, and consumption), which otherwise cannot be achieved by human labor or by use of ID tags. Firstly, I will talk about a technology to recognize multiple objects from a single image using feature matching of compact local descriptors, combined with a more recent Deep Learning-based recognition. It enables large number of objects to be recognized at once, which greatly reduces human labor and time for various product inspection and checking works. Using this technology, we have developed and commercialized the product inspection system in warehouses, the planogram recognition system for retail shop shelves, and the self-service POS system for easy-to-use and fast checkout in retail stores. Secondly, I will talk about the "Fingerprint of Things'' technology. It enables individual identification of tiny manufactured parts (e.g. bolts and nuts) by identifying images of their unique surface patterns, just like human fingerprints. We have built a prototype of mass-produced parts traceability system, which enables users to easily track down the individual parts using a mobile device. In the talk, I will explain the key issues in realizing these industrial applications of image-based object recognition technologies.