# Multi-Defect Detection Network for High-Voltage Insulators Based on Adaptive Multi-Attention Fusion
## 1 Introduction  
Amidst the swift progression of the economy, the escalating need for electrical power presents challenges in maintaining and repairing the power grid infrastructure. Insulators, serving as essential components of high-voltage transmission lines, deliver both electrical insulation and mechanical support [1]. Nonetheless, insulators are vulnerable to defects such as spontaneous explosion, fracture, and flashover, attributed to influences such as atmospheric conditions and mechanical stress. More importantly, insulator defects represent one of the primary sources of power system failures, responsible for over half of all incidents. Consequently, the expedient and precise detection and characterization of insulators and their defects carry substantial practical implications for maintenance and repair staff. It is acknowledged that the detection process of high-voltage insulators can be accomplished through conventional image processing algorithms, based on techniques such as background removal, masking, contouring, and BW conversion [2, 3]. Traditional image processing algorithms primarily rely on manually designed feature structures for the target. For instance, in the work by [PERSON] et al. [4], based on the spatial morphological consistency of insulators, significant detection was performed on inspection images to identify candidateregions of insulators. Subsequently, descriptors characterizing the insulator features were defined based on the projection curves obtained after binary segmentation of the candidate regions. This augmentation enables precise localization of insulator strings. However, this approach not only requires sufficient prior knowledge but also heavily depends on manually designed features, resulting in poor generalization and difficulty in application across different scenarios in electrical engineering. Owing to the advancements in deep learning technology [5; 6; 7], defect detection techniques grounded on convolutional neural networks (CNNs) [8] have gained popularity. In contrast with traditional insulator defect detection methodologies, deep learning-based approaches can discern defect areas more accurately, even amidst complex backgrounds or occlusions, and they do not demand additional information. Therefore, it is necessary to employ deep learning methods for insulator detection. The current deep learning-based target detection techniques can be bifurcated into two categories. The first encompasses two-stage deep learning target detection algorithms that utilize Faster R-CNN [9]. For instance, [PERSON] et al. [10] put forth an enhanced Faster R-CNN-based method to identify faults in key components of transmission lines. This method merges the Inception [11] and ResNet101 [12] networks to establish a feature extraction network and accomplishes accurate identification of defective parts via target positioning and classification. [PERSON] et al. [13] introduced a size-corrected CNN-based strategy for locating and classifying defects in insulator strings. This approach utilizes laser radars to compute the unmanned aerial vehicle (UVA) correction vector, ensuring consistent spatial dimensions of insulators. Furthermore, it employs Faster R-CNN for the purpose of defect detection. Despite this method providing high detection accuracy and excelling in the detection of small targets, its two-stage detection methodology introduces increased model complexity and a considerable number of parameters, which subsequently results in diminished detection speeds. The second category encompasses single-stage target detection algorithms, among which YOLO [14; 15; 16] is frequently employed. [PERSON] et al. [17] proposed an automatic insulator detection method using the YOLOv5 target detection model. This method effectively identifies and localizes insulator defects on transmission lines by implementing the K-means clustering technique. [PERSON] et al.