# Transmission Lines Insulator State Detection Method Based on Deep Learning
## 1 Introduction  
As the power industry continues to grow, the increasing number of transmission lines makes it critical to ensure their safe and stable operation for consistent electricity transmission. Insulators, crucial components of transmission lines, support the overhead wires while preventing current from grounding. Exposed to complex environments, insulators are prone to severe conditions like lightning strikes, ice accumulation, extreme temperatures, wind erosion, and other harsh factors that can cause flashovers, breakage, or even self-explosion. These states pose significant risks to the safety and reliability of transmission lines. Consequently, accurate detection of insulator state is of paramount importance in the field of power transmission line inspection [1, 2]. Given the vast scale of modern power grids, manual inspection of transmission lines or traditional methods is not only time-consuming but also prone to errors such as missed and false detections. This leads to slow detection speeds and reduced accuracy, making it challenging to meet the current operational demands of the power grid. With the rapid advancement of artificial intelligence, drones, robots, and other sophisticated equipment are increasingly being deployed in the power industry. This has led to the widespread adoption of drone-based inspection methods for transmission-lines insulators [3, 4, 5]. Byleveraging a large number of insulator images captured by drones, automatic inspection methods for assessing insulator conditions have been proposed. Among these, the use of target detection methods within computer vision technology to enable automated analysis of drone inspection images has become a significant research focus [6]. With the ongoing advancement of target detection methods, it is increasingly widely used in the supervised detection of insulator states. Target detection methods can be classified into two-stage methods and one-stage methods. The two-stage methods, such as Faster R-CNN [7] and Mask R-CNN [8], are notable for their high accuracy. For example, a recent study [9] achieved multi-target fault detection in aerial images by integrating an area-based non-maximal suppression method into Faster R-CNN and employing a cut-off detection scheme. Similarly, another study [10] proposed a method for identifying bird-related faults on transmission towers using a Fine Mask Regional Convolutional Neural Network (Fine Mask R-CNN). This method incorporates an information fusion module with multi-scale convolutional kernels to recover lost details, enabling it to detect and evaluate insulator faults caused by bird pecking and contamination from bird droppings. The one-stage target detection methods, such as SSD [11] and the YOLO series [12; 13; 14; 15], have gained significant popularity for their speed and efficiency. For instance, one study [16] introduced a lightweight MobileNet-SSD target detection network combined with a MobileNetV2-DeeplabV3+ image segmentation network to detect and segment insulator self-explosion faults. This combination provides a relatively lightweight solution suitable for UAVs and embedded systems. Another study [17] improved the YOLOv3 method by modifying the loss function with a Focal Loss and a balanced cross-entropy function, achieving 91.8% accuracy in locating and identifying insulator strings. Furthermore, a separate study [18] altered the Neck structure in the YOLOv5 framework from FPN+PAN to a BiFPN structure, introducing an adaptive weighted fusion module for both multi-scale and same-scale features. This adjustment resulted in a detection accuracy of 96.8%. In another approach, a study [19] replaced the backbone network of YOLOv5s with EfficientNet, resulting in a notable increase in accuracy for transmission-line insulator fault detection, with a reported accuracy of 98.5%.