## 5 Conclusions  
An Intelligent Fast Detection for Real-time Image Information in Industrial IoT is designed in this paper. We use WGAN to expand the training set, and we use k-means++ clustering to obtain a better-preset anchor box.  
The downsampling processes that cause information loss have been minimized by the VaryBlock module, which significantly boosts network performance. We trained and tested our method on the COCO and TUM datasets, and the detection accuracy is very impressive. Compared with the original YOLOv3-Tiny, the mAP of our method based on YOLOv3-Tiny is improved from 55.3% to 60.4%, especially the APS is increased by 8%. However, compared with YOLOv3-Tiny+WGAN, APS (increased by 2.6%), APM (increased by 1.7%), and APL (decreased by 0.1%), our method does not improve too much.  
In addition, for some objects that are difficult to be recognized by object detection, such as non-rigid objects, the method in this paper has certain limitations, and it is still difficult to accurately recognize. In addition, the addition of new modules leads to an increase in the number of network layers, and the increase in parameters leads to a slower convergence speed. In future work, we plan to improve on five aspects: (1) optimizing the detection of non-rigid objects; (2) applying the method in this article to semantic SLAM to optimize the visual odometer of traditional SLAM; (3) using XAI [59] tools to better explain, evaluate and improve our model; (4) applying this method to Edgex Foundry (an kind of edge computing framework); (5) according to the idea of GAN, we plan to explore a better expansion method of the image dataset.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & **SSD(300)** & **RCNN** & **Faster RCNN** & **YOLOv3-Tiny** & **Ours(v3)** & **YOLOv4-Tiny** & **Ours(v4)** \\ \hline keyboard & 45.4 & 44.9 & 48.7 & 45.5 & 57.2 & 52.3 & **61.2** \\ book & 33.7 & 38.2 & 40.7 & 52.2 & 49.5 & 60.5 & **66.8** \\ plant & 56 & 60.2 & 65.8 & 56.5 & 59.6 & 56.1 & **66.4** \\ cans & 28.8 & 29.4 & 36.8 & 55.1 & 58.9 & 57.4 & **64.2** \\ mouse & 20.7 & 15.2 & 45.9 & 56.1 & 56.8 & 56.7 & **63.9** \\ chair & 36.8 & 37.6 & 54.8 & 60.1 & 56.4 & **61.4** & 55.3 \\ tvmonitor & 56.7 & 41.6 & **60.6** & 42.1 & 54.5 & 57.5 & 57.3 \\ cup & 35.8 & 22.8 & 50.7 & 51.7 & 68.6 & 53.4 & **70.3** \\ person & 35.3 & 39.9 & 56.4 & 62.1 & **64.1** & 62.4 & 61.2 \\ laptop & 40.7 & 34.4 & 50.2 & 49.0 & 56.5 & 53.6 & **58.7** \\ mAP(\%) & 39.2 & 39.4 & 51.1 & 51.7 & 59.4 & 58.9 & **62.5** \\ FPS & 40 & 37 & 49 & 74 & 65 & **85** & 81 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of the AP value and speed of different networks only on the TUM dataset.  
Figure 9: The training loss curves of our proposed method and YOLOv3-Tiny.  
**Author Contributions:** Conceptualization, Y.L. and H.Z.; Methodology, Y.L. and H.Z.; Validation, H.Z.; Formal Analysis, H.Z.; Software, Y.W.; Writing--Original Draft Preparation, Y.W., N.X.; Data Curation, Y.W.; Writing--Review and Editing, Y.L., H.Z. and Y.W.; Project Administration, H.Z.; Investigation, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China (Grant 61963017), in part by Shanghai Educational Science Research Project (Grant C2022056), and in part by Jiangxi Province's Outstanding Youth Planning Project (Grant 20192 BCBL23004).  
Not applicable.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.