## 1 Introduction
By integrating feature maps of different sizes, these models enhance their ability to extract features at various scales, ultimately improving detection accuracy. In the field of deep-learning-based visual detection for power equipment, significant efforts are being made to improve detection performance across a range of electrical devices. [PERSON] et al. [12] put forth a object detection model based on an improved faster R-CNN, tailored for the automatic detection of five types of power equipment in substations. This method involves modifying the feature extraction network and adjusting the aspect ratios of anchor boxes to elevate the accuracy when identifying elongated equipment. To tackle the challenges presented by the diverse shapes and sizes of distribution equipment, [PERSON] et al. [13] introduced a multi-device detection method for distribution line inspection based on YOLOx-s. This innovative approach enables the simultaneous recognition of multiple types of power equipment, thereby bolstering the intelligence of autonomous UAV inspections of distribution lines. These studies predominantly sought to address the visual detection challenges of electrical equipment with significant appearance variations. typically achieve a light weight by modifying the backbone network and redesigning the detection head, thereby reducing model parameters, while preserving the detection accuracy. This research provides valuable insights for developing real-time object detection models in power system scenarios. Overall, existing research has primarily focused on improving the accuracy, multi-scale detection, and real-time performance of power equipment detection models. These improvements can be summarized as follows: (1) Multi-scale fusion methods have been introduced to enhance a model's ability to recognize objects of varying sizes; (2) Convolutional modules and attention mechanisms have been designed to strengthen feature extraction, thereby improving model accuracy; (3) Lighter network architectures have been proposed to accelerate model inference, thereby enhancing real-time performance. It is important to note that isolation switch status detection differs from other electrical equipment detection tasks, due to its unique characteristics. First, compared to other electrical devices, isolation switches have a relatively elongated shape with a large length-to-width ratio. Second, the structure of isolation switches is variable; they exhibit significant visual differences between open and closed states, and may also appear in a separated state. Third, in monitoring scenarios, the scale of isolation switches can vary within the same image, due to differences in the distance from the monitoring equipment. Finally, power monitoring systems often generate large volumes of image data. To ensure faster system response and reduce data transmission delays, lightweight models must be deployed at the edge to process images in real time. To address the aforementioned challenges, this paper first proposes an efficient mobile inverted bottleneck convolution (EMBC) module within the backbone network. This module leverages an ELA attention mechanism [16] to enhance the feature extraction capability of the detection model for complex backgrounds. The ELA attention mechanism focuses on the spatial features of object positions in both the horizontal and vertical directions. By independently processing the feature vectors of each direction, it activates attention weights, guiding the model to focus on the location information of the isolation switch in different states. Second, an efficient-RepGDFPN feature fusion module is employed, which uses lightweight convolutional modules and a fast normalized fusion method to enhance the model's multi-scale feature fusion capability. Finally, depthwise separable convolutions are used to replace the traditional convolutions, achieving a lightweight model for isolation switch detection and improving the real-time inference performance.