# A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend
## 1 Introduction  
Transmission poles and towers play a crucial role in the safe operation of the power system by supporting overhead power transmission lines. However, due to their widespread distribution, these structures are often constructed in areas prone to landslides, subsidence, riverbeds, and hillsides. Extreme weather can cause transmission tower tilting, deformation, or collapse, leading to significant power grid failures. Over the years, various methods for monitoring the status of towers have been developed. Video inspection of the substation to detect tower inclination has been proposed [1; 2; 3]. [PERSON] and [PERSON] proposed a method for the dynamic monitoring of the towering state [4]. In spite of its innovative approach, it lacks real-time analysis functions, and it is difficult to respond effectively to emergencies due to the limited computing power on edge computing servers. Further, using the existing power line corridor, some researchers removed the tower base and used a vertical stratification method and a convex shell algorithm to determine the tower inclination when the point cloud tower is scarce [5]. However, this method focuses on 3D data presentation and does not respond in real time. [PERSON] et al. used an optical fiber sensing technology to monitor the stress deformation and tilting angle of the power tower, but the accuracy was not high [6]. Unmanned Aerial Vehicles (UAVs) equipped with video cameras were also used to detect tower tilting by processing the captured images [7]. Smart antennas have been used to detect the deformation of ultra-high transmission towers [8], where the detected deformation information can be directly used to assess the health status of the towers. In addition to the video camera and smart antennas, a tension and tilt-monitoring device was developed by [PERSON] and [PERSON] to detect the imbalance of the poles and towers [9]. The health status of towers can also be analyzed by installing an unbalanced tension sensoron the insulator [3] or using a PVDF piezoelectric sensor [4]. In addition to installing sensors on power systems, remote sensing technologies such as the Beidou Satellite have been used for online monitoring [9]. [PERSON] and [PERSON] [10] designed a transmission line pole, and tower imbalances and the current state of the tower were analyzed and detected [3]. The health status of a high-voltage transmission tower has also been studied [11]. Additionally, the Beidou satellite has been used to investigate an online monitoring system [9]. While these methods are effective, they face challenges in terms of low accuracy, poor real-time performance, or high cost of operation. Inspired by previous research using a monocular camera to monitor power lines against tree intrusions, this paper proposes using monocular cameras are used to monitor the tilting status of the towers in this paper. Monocular cameras are inexpensive and, when paired with an edge-computing module to process and transmit the images, can provide online and real-time monitoring. The edge-computing module can also be connected to a data center for further analysis. Currently, common methods for detecting the posture of towers include periodic manual measurement, unmanned aerial vehicle inspection, and video surveillance [12; 13; 14]. Although these methods can realize a certain level of monitoring of the transmission channel, they often have limitations, including low accuracy and poor real-time performance. * Manual measurement requires on-site measurement with instruments, which is cumbersome and requires a lot of workforce and material resources. It is also complex and poses specific security risks due to the geographical conditions of transmission lines. UAV monitoring uses airborne laser radar to build three-dimensional point clouds, which are then projected onto two-dimensional planes and analyzed using correlation algorithms [15]. However, this method has a high cost and does not provide real-time monitoring.