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Object Detection for Cargo Unloading System Based on Fuzzy C Means

Sunwoo Hwang1, Jaemin Park1, Jongun Won2, Yongjang Kwon3, Youngmin Kim1,*

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 this Special Issue: Analysis, Processing, and Applications of Fuzzy System and Deep Learning)

Computers, Materials & Continua 2022, 71(2), 4167-4181.


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.


Cite This Article

S. Hwang, J. Park, J. Won, Y. Kwon and Y. Kim, "Object detection for cargo unloading system based on fuzzy c means," Computers, Materials & Continua, vol. 71, no.2, pp. 4167–4181, 2022.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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