ABSTRACT
THE VISUAL WEIGHING METHOD OF MINING DUMP ATRUCK BASED ON RESNET
Journal: Matrix Science Mathematic (MSMK)
Author: Kai Bai, Likun Zhao, Wenqing Che, Beijun Guo, and Zhanlong Li
This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
DOI: 10.26480/msmk.02.2024.53.58
Intelligent weighing systems play a significant role in guiding coal production and assisting production decisions. However, the traditional weighing method not only cannot guarantee efficiency, but also is prone to artificial fraud, causing economic losses to enterprises. In view of the needs of industrial production, this paper studies a visual weighing method for mining dump trucks with ResNet deep learning network as the core. Firstly, the collected dataset is processed by the Resize function and the Blend function, and then the ResNet neural network is trained with the processed dataset, and finally the cross-entropy loss function and Adam optimization strategy are used to improve the recognition accuracy. The results show that the final recognition accuracy is 60%.
Pages | 53-58 |
Year | 2024 |
Issue | 2 |
Volume | 8 |