###### Abstract
Drones may be used for on-demand inspections; however, this is not a viable solution for continuous monitoring. Artificial Intelligence, computer vision, edge computing, and Artificial Intelligence of Things (AIoT) technologies have taken off in the recent few years and are efficiently addressing many surveillance-/detection-related real-world problems (e.g., pedestrian detection [16], wildlife monitoring [17], culvert blockage detection [18], plastic bag contamination detection [19]). Detecting pipeline easements against external interference using computer vision poses a unique challenge with no existing literature or visual dataset. However, several studies have explored computer vision techniques for construction machinery detection, which is highly relevant to this problem. In 2018, [PERSON] et al. [20] used an R-FCN object-detection model in the context of detecting construction equipment in a dataset consisting of around 3000 images extracted from the ImageNet dataset. The trained model was able to achieve a mAP of 95% for the excavator class. Later, in 2019, [PERSON] and [PERSON] [21] used convolutional neural network (CNN)-based object-detection (i.e., RetinaNet) and -tracking algorithms for the activity analysis of earthmoving equipment. These analyses were based on only 10 videos captured at very specific construction sites at the ground level. For this custom dataset, a detection AP of 97% was reported for the excavator class. Although a high performance was reported, the model was trained for two classes (excavators, dump trucks), making it a relatively easier problem in comparison to the presented 13-class problem. In a recent research work, [PERSON] and [PERSON] [22] used the You Only Look Once version 3 (YOLOv3) object-detection model with Simple Online and Real-time Tracking (SORT) to track construction machines. A dataset of around 10,000 images named the Alberta Construction Image Dataset (ACID) [23] of 10 types of construction machines with excavators as one of the classes was used for training the object-detection model. The authors did not report the detection model's accuracy for each separate from the tracking. Despite not directly addressing pipeline easement detection, these studies highlight the potential of object-detection models in similar contexts, suggesting the promise of addressing this challenge from a computer vision perspective. Notably, none of the literature works discussed deployment on edge computing hardware in the field, which is crucial for an end-to-end solution. This paper proposes to develop an end-to-end AIoT solution to alert the pipeline operator of a potential interference threat in real time. The idea presented is to develop a smart visual sensor capable of processing the live video feed from a local camera, detect the potential interference threat using a state-of-the-art computer vision algorithm, and send the alert (i.e., a message notifying the operator about the threat and containing the information about the threat type, time stamp, and device_id) to the pipeline operator in real time to avoid an accident. The development of the AI system to automatically detect the interference threat is the core of this solution and is based on the object-detection model. For this research, two object-detection models including YOLOv4 and DETR with Improved deNoising anchOr boxes (DINO) were implemented and compared. To train the models, a custom dataset (i.e., Pipeline Visual Threat Assessment (Pipe-VisTA)) was developed from multiple resources and annotated for training. The anticipated contributions are as follows:  
1. The development of the custom Pipe-VisTA dataset consisting of 10,181 images to facilitate the training of the computer vision object-detection model. 2.