## 5 Conclusions and Future Work  
The intelligent visual sorting system plays important role in intelligent manufacturing, in which artificial intelligence and computer vision technologies improve the intelligent and unmanned development of the industry. We establish a deep CNN-based multi-crane visual sorting system with cloud PLCs in a 5G environment. A YOLOv5-based algorithm and a Chinese character recognition network were developed to significantly locate and recognize materials from the original image. mAP of YOLOv5 on validating set is up to 98.85%, and the confidence score is close to 1, which significantly shows the performance of the location module. The accuracy of Chinese character recognition is also up to 99.43% on testing set, which fully met the requirement of different material sorting in the industry. Moreover, cloud PLCs are investigated to be flexibly deployed in the terminal and in the cloud, which allows low-level devices to cooperate with each other. Therefore, the control scheduling method is designed in cloud PLCs that cooperates with AI algorithms to control the multi-crane in minimum time. The average time of sorting is about 3 s, which contains the whole process of the crane from starting to sorting completion. The real experimental platform of the multi-crane visual sorting system verifies the performance of the proposed methods.  
Although the experimental results perform dramatically, there is much room to improve the multi-crane sorting system. In the industry, there are massive data streams that are transmitted by a 5G network. We will design 5G and TSN bridge as a transmission layer to highly guarantee deterministic communication. In addition, we will integrate cloud  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time** & **Prediction Time (s)** & **Real Time (s)** & \(|\Delta t|\) \\ \hline
1 & [(1, 2.972), (2, 3.282), (3, 3.301), (4, 3.616)] & 2.756 & 0.216 \\
2 & [(1, 2.804), (2, 3.179), (3, 3.237), (4, 3.648)] & 3.016 & 0.212 \\
3 & [(1, 3.146), (2, 3.160), (3, 3.179), (4, 3.398)] & 3.056 & 0.090 \\
4 & [(1, 2.958), (2, 3.037), (3, 3.055), (4, 3.370)] & 3.064 & 0.106 \\
5 & [(1, 2.838), (2, 3.179), (3, 3.237), (4, 3.648)] & 3.036 & 0.198 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The consuming time of the crane for the sorting task with scheduling strategy.  
Figure 9: The experimental diagram of the crane visual sorting system.  
PLCs and an AI platform on the same server in the cloud for stable performance. We will develop some scheduling method to control the low-level devices for low energy and high efficiency. Furthermore, we will continue to improve the accuracy of AI algorithms that can be used in real industrial applications.  
Conceptualization, M.F., J.W., Z.M. and W.L.; methodology, M.F., Q.W., J.W. and L.S.; software, M.F. and Q.W.; validation, M.F., W.G. and C.Z.; formal analysis, M.F. and D.W.; investigation, M.F.; resources, Z.M.; data curation, C.Z. and Q.L.; writing--original draft preparation, M.F.; writing--review and editing. J.W., Q.W. and D.W.; visualization, M.F.; supervision, J.W.; project administration, J.W.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
This work is supported by National Key Research and Development Program under Grant 2020 YFB1708800, Guangdong Key Research and Development Program under Grant 2020B0101130007, Interdisciplinary Research Project for Young Teachers of USTB (Fundamental Research Funds for the Central Universities) FRF-IDRY-21-005, Fundamental Research Funds for Central Universities under Grant FRF-MP-20-37, Guangdong Basic and Applied Basic Research Foundation under Grant 2021A1515110577, and China Postdoctoral Science Foundation under Grant 2021M700385. Central Guidance on Local Science and Technology Development Fund of ShanXi Province under Grant YDZJSX2022B019.  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors declare no conflict of interest.