Open Access

ARTICLE

Object Detection for Cargo Unloading System Based on Fuzzy C Means

by Sunwoo Hwang¹, Jaemin Park¹, Jongun Won², Yongjang Kwon³, Youngmin Kim^1,*

1 Department of Systems Engineering, Ajou University, Suwon, 16499, Korea
2 New Transportation Innovative Research Center, Korea Railroad Research Institute, Uiwang, 16105, Korea
3 Innovative Transportation and Logistics Research Center, Korea Railroad Research Institute, Uiwang, 16105, Korea

* Corresponding Author: Youngmin Kim. Email:

(This article belongs to the Special Issue: Analysis, Processing, and Applications of Fuzzy System and Deep Learning)

Computers, Materials & Continua 2022, 71(2), 4167-4181. https://doi.org/10.32604/cmc.2022.023295

Received 02 September 2021; Accepted 20 October 2021; Issue published 07 December 2021

Abstract

With the recent increase in the utilization of logistics and courier services, it is time for research on logistics systems fused with the fourth industry sector. Algorithm studies related to object recognition have been actively conducted in convergence with the emerging artificial intelligence field, but so far, algorithms suitable for automatic unloading devices that need to identify a number of unstructured cargoes require further development. In this study, the object recognition algorithm of the automatic loading device for cargo was selected as the subject of the study, and a cargo object recognition algorithm applicable to the automatic loading device is proposed to improve the amorphous cargo identification performance. The fuzzy convergence algorithm is an algorithm that applies Fuzzy C Means to existing algorithm forms that fuse YOLO(You Only Look Once) and Mask R-CNN(Regions with Convolutional Neuron Networks). Experiments conducted using the fuzzy convergence algorithm showed an average of 33 FPS(Frames Per Second) and a recognition rate of 95%. In addition, there were significant improvements in the range of actual box recognition. The results of this study can contribute to improving the performance of identifying amorphous cargoes in automatic loading devices.

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Citations