## 8 Conclusions  
An AIoT smart device has been successfully developed and deployed in the field with the capability to detect an external interference threat to pipelines and alert the operator to avoid potential accidents. Two object-detection models (i.e., YOLOv4, DINO) were trained using the custom-developed Pipe-VisTA dataset to detect the threat from 13 categories. The proposed solution aimed to capture the image using a camera, detect the threat using the trained object-detection model, transmit the threat alert to the Fleet Portal using LoRaWAN, and host the alert information via the satellite network on a dashboard accessible to the operator/manager. Among the trained models, DINO was able to achieve the best validation and test mAP of 0.671 and 0.712, respectively. The tractor (i.e., 0.829), boring_rig (i.e., 0.823), and auger (i.e., 0.822) threat categories were identified as the best detected, while clay_delver (i.e., 0.256) and bobcat (i.e., 0.392) were reported as the worst.  
Figure 10: Location of the deployed AIoT devices for the SEA Gas use-case.  
However, given the computational constraint of the NVIDIA Jetson Nano hardware for deployment, the simpler architecture of YOLOv4 with TensorRT optimization support available was used. The YOLOv4 model was able to achieve a validation and test mAP of 0.631 and 0.618, respectively. The developed AIoT device was tested and validated in the field to ensure the hardware's and software's functionality. The fully tested solution was deployed for a SEA Gas use-case site of 10 kilometers along the Murray Bridge section. In total, 48 AIoT devices and three Fleet Portals were deployed to cover the pipeline in three regions ensuring the line-of-sight communication from the devices to the portals. In the future, the performance of the AI model can be further improved by adding more images from the field in collaboration with pipeline companies covering a wide range of weather and lighting conditions. Furthermore, the camera system can be improved in terms of lens/image quality and power consumption. The power optimization will give longer battery times, which will enable tracking the duration of the threat in field. The dataset imbalance problem can be addressed using advanced synthetic-data-generation techniques including Generative AI models and the NVIDIA _Omniverse_ platform.  
Conceptualization, J.B., U.I. and G.M.; methodology, J.B. and U.I.; software, J.B., U.I. and G.M.; validation, J.B., U.I. and G.M.; formal analysis, J.B., U.I. and G.M.; investigation, J.B., U.I. and G.M.; resources, J.B., U.I. and G.M.; data curation, U.I., J.B. and G.M.; writing--original draft preparation, U.I.; writing--review and editing, J.B. and G.M.; supervision, J.B. All authors have read and agreed to the published version of the manuscript.  
This work is funded by the Future Fuels CRC, supported through the Australian Government's Cooperative Research Centres Program.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The cash and in-kind support from the industry participants is gratefully acknowledged.  
Author [PERSON] was employed by the APA Group. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest