###### Abstract  
High-pressure pipelines are critical for transporting hazardous materials over long distances, but they face threats from third-party interference activities. Preventive measures are implemented, but interference accidents can still occur, making the need for high-quality detection strategies vital. This paper proposes an end-to-end Artificial Intelligence of Things (AIoT) solution to detect potential interference threats in real time. The solution involves developing a smart visual sensor capable of processing images using state-of-the-art computer vision algorithms and transmitting alerts to pipeline operators in real time. The system's core is based on the object-detection model (e.g., You Only Look Once version 4 (YOLOv4) and DETR with Improved deNoising anchor boxes (DINO)), trained on a custom Pipeline Visual Threat Assessment (Pipe-VisTA) dataset. Among the trained models, DINO was able to achieve the best Mean Average Precision (mAP) of 71.2% for the unseen test dataset. However, for the deployment on a limited computational-ability edge computer (i.e., the NVIDIA Jetson Nano), the simpler and TensorFlow-optimized YOLOv4 model was used, which achieved a mAP of 61.8% for the test dataset. The developed AIoT device captures the image using a camera, processes on the edge using the trained YOLOv4 model to detect the potential threat, transmits the threat alert to a Fleet Portal via LoRaWAN, and hosts the alert on a dashboard via a satellite network. The device has been fully tested in the field to ensure its functionality prior to deployment for the SEA Gas use-case. The AIoT smart solution has been deployed across the 10 km stretch of the SEA Gas pipeline across the Murray Bridge section. In total, 48 AIoT devices and three Fleet Portals are installed to ensure the line-of-sight communication between the devices and portals. e 2024 22 2024 22 2024 22 2024 22 2024 22 2024 22 2024 22 2024 22 2024 22 2024 22 2024 22 2024 22 2024 22 2024 22 2024 22 2024 22 2024 22 2024 22024 22024  
Under AS 2885 [9], pipeline licensees are required to complete a Safety Management Study (SMS) to demonstrate that adequate physical and procedural measures are in place to protect the pipeline [9]. In this context, there are several measures taken to avoid interference accidents and to ensure the safe operations of the pipeline including pipeline location marking (e.g., above-ground markers and signs) [10], regular visual and internal inspections (e.g., on-ground patrolling, drones, smart pigs) [11], training and education programs (e.g., awareness workshops, trainings) [12], coordination with local authorities (e.g., work plans, information sharing), and implementing safety measures (e.g., fencing, signage) [13]. Despite the implementation of so many preventive measures, interference accidents can still occur for several reasons including human error, equipment failure, lack of awareness, extreme weather, and malicious activities [4;14]. Conventional methods of periodic patrolling to detect third-party activity near pipelines are restricted in their effectiveness due to the limited time-frame in which threats can be detected. As population growth and urban expansion lead to increased easement activity near pipelines, the limitations of current patrolling methods become more pronounced [15]. These limitations are not unique to Australia and are likely to be a common issue in the global pipeline and linear infrastructure industry. Therefore, the need for high-quality detection strategies is vital for the few occasions where preventative controls fail to avoid potentially catastrophic consequences. Satellite photogrammetry and drone-based solutions are one potential approach; however, these come with several operational limitations. Satellite-photogrammetry-based solutions are highly impacted by the weather conditions, and it expensive to obtain high-resolution images and in real time. The drone-based solution may work at the local scale; however, they are limited to short operational times.