## 1 Introduction  
Isolation switches are critical components in power systems. Accurate and reliable determination of their operational status is essential for power grid security. With the rapid development of smart grids and the increasing prevalence of unmanned substations, there is a growing need for real-time monitoring and automatic identification of the operational status of isolation switches [1]. Manual inspections traditionally consume both time and effort, while also facing challenges in maintaining accuracy and timeliness, particularly during unfavorable weather conditions. With the rise of automation and intelligent inspection technologies, computer vision and pattern recognition techniques are increasingly being applied in modern power systems to assist in monitoring the operational status of isolation switches [2, 3, 4]. In the early stages, most research on visual isolation switch detection methods primarily focused on traditional machine learning methods. These methods first design feature descriptors for isolation switches, such as histogram of oriented gradients (HOG) and local binary patterns (LBP), to extract image feature information. Traditional machine learning classifiers, such as support vector machines (SVM) and random forests, are designed to determine the operational status of isolation switches [5]. Compared to deep-learning-based methods, traditional machine learning methods do not necessitate large image datasetsof isolation switches for model training and are comparatively easier to deploy on inspection robots. However, in complex power monitoring scenarios, the isolation switch state recognition results of traditional machine learning methods are susceptible to factors such as lighting variations, occlusions, and diverse viewpoints. The dual-layer cellular neural network (CNN) with a constant template proposed by [PERSON] et al. [6] is well-suited for generating self-organizing patterns and can simulate complex phenomena, offering a potential solution for detection in complex and dynamic power scenarios. In recent years, the rise of deep learning has led to significant progress in the intelligent identification of power system equipment. Object detection algorithms based on deep learning, such as YOLO [7], SSD [8], and Faster R-CNN [9], have been widely used to identify the status of power equipment. These algorithms have demonstrated exceptional performance in detecting objects with nonlinear and high-dimensional features. The accuracy and robustness of detection results have significantly improved. Currently, research on deep-learning-based methods for power equipment detection primarily focuses on the identification and fault detection of other types of power equipment, while little research focuses on isolation switch state detection. Numerous recent studies on deep-learning-based power equipment recognition have concentrated on identifying electrical devices of various sizes in monitoring scenarios, particularly in environments such as substations. The diverse morphological differences in the equipment within these images pose a significant challenge for visual detection models in adapting to objects of different scales, often resulting in suboptimal recognition outcomes. [PERSON] et al. [10] proposed an enhanced YOLOv5-based visual fault detection algorithm for substation equipment, which adjusts the combination of multi-level features in the network through a floating adaptive weighted fusion strategy, enabling adaptive learning for objects of different scales. Similarly, [PERSON] et al. [11] introduced a YOLOX++ detector, based on YOLOX, which utilizes a multi-scale cross-stage partial network (MS-CSPNet) to fuse multi-scale feature information and expand the receptive field for objects through channel combinations, thereby optimizing the localization of small objects. The primary focus of these studies was to enhance the multi-scale fusion network in the neck of object detection models.