## 6 Conclusions  
In this paper, we propose a lightweight network called DAMP-YOLO. It utilizes deformable CSP bottleneck (DCB), aggregated triplet attention (ATA), and meter data augmentation (MDA) to enhance the YOLOv8 network and employs network pruning (NP) to reduce the number of model parameters. The proposed modules enable the network to better capture objects with similar shapes and structures through deformable convolutional kernel offsets and focus attention on global information interaction across different dimensions. The experimental results demonstrate the superiority of the DAMP-YOLO network compared to other state-of-the-art methods in terms of precision, recall, and mAP, with a significant reduction in model size. This makes DAMP-YOLO a promising solution for real-time meter reading recognition when using resource-constrained devices. Furthermore, we designed a pipeline and deployed it on a Jetson TX1 intelligent car to achieve real-time recognition of meter readings. The successful deployment of this on the Jetson TX1 intelligent car demonstrated the practicability of our method in real-world applications. In the future, we can explore further optimizations of the DAMP-YOLO network for faster inference speed, such as post-training quantization, to enhance its efficiency and reduce  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **mAP@50:95/\%** & **Parameters/M** & \
\begin{tabular}{c} **Inference/ms** \\ **(MX350)** \\ \end{tabular} &
\begin{tabular}{c} **Inference/ms** \\ **(Jetson TX1)** \\ \end{tabular} \\ \hline YOLOv8s & 88.61 & 11.16 & 44.2 & 184.5 \\ SSD300 & 82.44 & 25.35 & 86.2 & - \\ Retinanet & 84.10 & 20.02 & 267.4 & - \\ TOOD & 84.61 & 32.05 & 414.3 & - \\ \hline MobileNetV2-SSDLite & 83.65 & 3.21 & 25.7 & - \\ YOLOv8n & 87.92 & 3.15 & 23.5 & 76.0 \\ YOLOv8n-MobileNetV3 & 87.58 & 2.25 & 23.7 & 83.9 \\ YOLOv8n-ShuffleNetV2 & 87.31 & 1.86 & 18.6 & 62.1 \\ YOLOv8n-RepVGG & 87.74 & 2.89 & 25.1 & 78.1 \\ \hline DAM-YOLO & 88.77 & 3.46 & 40.2 & 136.5 \\ DAMP-YOLO & 88.82 & 2.40 & 37.3 & 129.6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of inference speed for the meter reading recognition dataset.  
Figure 20: The inference results for the Jetson TX1 intelligent car for different models. (**a**) the results of YOLOv8n, and (**b**) the results of DAMP-YOLO, showing the stability and robustness of the proposed model in light interference.  
memory requirements. Additionally, we can investigate the network's performance in real-world scenarios to further validate its applicability in practical applications.  
Conceptualization, S.Z.; funding acquisition, X.Z. and W.W. and F.W.; methodology, S.Z. and Z.C.; software, S.Z. and Q.L.; data curation, W.W.; formal analysis, X.Z.; writing--original draft, S.Z.; writing--review and editing, S.Z.; visualization, Q.L. and Y.G.; supervision, X.Z.; project administration, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the 2022 Scientific Research Project of Beijing Municipal Education Commission (Grant No. KM202210017006), the 2020 Scientific Research Project of Beijing Municipal Education Commission (Grant No. KM202010017011), and the National College Student Innovation and Entrepreneurship Training Program (Grant No. 2023J00070).  
Not applicable.  
The data presented in this study are publicly available on Zenodo at: [[https://doi.org/10.5281/zenodo.830501](https://doi.org/10.5281/zenodo.830501)]([https://doi.org/10.5281/zenodo.830501](https://doi.org/10.5281/zenodo.830501)) (accessed on 26 September 2023) (floodX Flooding Images).  
The authors thank the editor and anonymous reviewers for providing helpful suggestions for improving the quality of this manuscript.  
The authors declare no conflict of interest.