# Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m
## 1 Introduction  
High-voltage transmission lines play a crucial role in ensuring the safety and stability of electricity transmission [1]. With the rapid development of the Chinese economy, China's power generation accounted for approximately 30.34% of the world's total in 2022, making its high-voltage transmission line network the largest globally [2]. These transmission lines traverse diverse and dynamic environments, often passing through residential and commercial areas, making them susceptible to the attachment of various foreign objects such as balloons, kites, fabrics and plastic waste [3]. Additionally, in natural settings like forests, birds nesting and twigs can challenge the safety of the transmission lines [4]. Suppose foreign objects on the transmission lines are not promptly detected and cleared: in that case, they can lead to accidents such as single-phase grounding and inter-phase short circuits, severely affecting the regular operation of the transmission lines, jeopardizing human safety, and resulting in casualties like accidental electrocution and fatalities [5].  
Early detection of foreign objects on transmission lines relied predominantly on manual inspections, a method characterized by high labor intensity and inefficiency. With the development of computer vision technology and the widespread application of unmanned aerial vehicles (UAVs), UAVs can capture the transmission lines' images. Then, the imagescan be analyzed using computer vision techniques to automatically identify anomalies, presenting a significant advancement in line inspection methodologies [6; 7].  
Currently, target detection research on transmission lines has mainly focused on target detection of principal components, foreign-object detection, smoke detection, insulator defect detection, etc. Among them, foreign-object detection mainly focuses on four types of foreign objects, namely balloons, bird nests, kites, and plastic trash. However, in the plateau mountainous environment of Yunnan Province, China, slender twigs and large birds also seriously interfere with the regular operation of high-voltage transmission lines. Therefore, slender twigs and birds are added to our detection database. The existing methods cannot effectively solve the problems faced by the Yunnan Branch of China Southern Power Grid Co., Ltd., so our research has specific value.  
This paper's contributions are as follows:  
1. Based on the data provided by the Yunnan Power Science Research Institute in the Yunnan Branch of China Southern Power Grid Co., Ltd., a foreign object dataset of power transmission lines was established. This dataset includes six types of objects: trash, twigs, nests, kites, birds, and balloons. In this database, foreign objects are occluded, with multiple types, large-scale changes, and complex backgrounds. The detection accuracy of existing methods is not high.
2. We propose a foreign-object-detection model for high-voltage transmission lines based on improved YOLOv8m. The experimental results show that our model has achieved high accuracy and can meet the needs of practical applications.