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Vision-based object classification using deep learning for inventory tracking in automated warehouse environment

Patel, AD and Chowdhury, AR (2020) Vision-based object classification using deep learning for inventory tracking in automated warehouse environment. In: International Conference on Control, Automation and Systems, 13-16 October 2020, Busan; South Korea, pp. 145-150.

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Official URL: https://dx.doi.org/10.23919/ICCAS50221.2020.926839...

Abstract

To achieve automatic inventory management in warehouses, it is necessary to identify items. Barcodes and RFID tags are traditional approaches to solve this problem but both of them suffer from limitations. This research paper presents a vision-based method using a deep convolutional neural network to classify different items stored in a warehouse for the purpose of inventory management. The proposed method uses residual learning and employs ResNet-50 network architecture. It achieves a high accuracy of 98.94 on the dataset created by the authors consisting of 1450+ images of machine parts, by utilizing data augmentation and transfer learning. It runs at 4 frames per second (FPS), making it suitable for other real-time applications as well. © 2020 Institute of Control, Robotics, and Systems - ICROS.

Item Type: Conference Paper
Publication: International Conference on Control, Automation and Systems
Publisher: IEEE Computer Society
Additional Information: cited By 0; Conference of 20th International Conference on Control, Automation and Systems, ICCAS 2020 ; Conference Date: 13 October 2020 Through 16 October 2020; Conference Code:165535
Keywords: Automation; Convolutional neural networks; Deep neural networks; Inventory control; Learning systems; Network architecture; Object tracking; Transfer learning; Warehouses, Automated warehouse; Data augmentation; Inventory management; Inventory tracking; Object classification; Real-time application; Traditional approaches; Vision-based methods, Deep learning
Department/Centre: Division of Mechanical Sciences > Centre for Product Design & Manufacturing
Date Deposited: 11 Feb 2021 06:28
Last Modified: 11 Feb 2021 06:28
URI: http://eprints.iisc.ac.in/id/eprint/67532

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