Automatic inventory management and tracking by deep learning in industrial environments

Résumé

Inventory management for large-scale warehouses has become one of the main challenges in the industry, due to the high demand for consumer products from online and local markets. Therefore, technology must be included to automate inventory management. In this thesis, we will focus on the acquisition and processing of images in an industrial environment. This will include new solutions for the industry. It will include in-depth learning about embedded systems using sensors and wireless equipment. The objective of this project is to apply the detection and classification of objects with the recognition of barcodes, QR codes, optical characters (OCR) and the detection of packages and pallets inside warehouses. On this project, we will create new deep learning models implemented on custom developed hardware that will be compatible with the industry domain. We will provide an automation service with low weight and low cost without thermal problems in the hardware equipment. The result of this project is to obtain an automated drone that will report warehouse inventory analysis in real time regardless of the size, layout and environment of the warehouse.

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