## 6 Conclusions  
In the recognizing the open and closed states of isolation switches, traditional visual detection methods often struggle with a low accuracy, poor adaptability, and insufficient robustness in complex environments. To address these challenges, we proposed a novel algorithm, EMB-YOLO, specifically designed for isolation switch state recognition. Given the characteristics of the isolation switch dataset, we designed an efficient mobile inverted bottleneck convolution module to extract features in complex environments, while reducing the computational costs. This module was specifically designed for isolator switch detection in complex environments. Its novelty lies in combining depthwise separable convolution with pointwise convolution and integrating the efficient local attention (ELA) mechanism. This enhances the model's ability to extract key switch features along the vertical and horizontal axes, which is crucial for power equipment monitoring. The efficient-RepGDFPN fusion network was also utilized to integrate multi-scale features, effectively addressing the scale diversity problem in isolation switch images. Our model can also be deployed on inspection robots or edge cameras, making it more convenient than traditional visual inspections. For power inspections, this reduces inspection time and labor costs. Especially in large-scale power equipment inspections, it enables automated processing, allowing inspection personnel to monitor the status of isolator switches at any time from a central control center. This reduces the workload and time required for manual inspections. The EMB-YOLO algorithm mainly relies on the shape features of isolator switches and is relatively insensitive to the influence of texture and color. Therefore, it can even maintain good performance under poor lighting conditions. However, the model still encounters challenges in certain situations. Firstly, when the isolator switch is partially obscured by wires, structural components, or nearby equipment, the detection performance decreases significantly, especially when more than 70% of the switch is obscured. In such cases, the model may fail to detect the switch or misidentify its state. This is because the model relies on distinct vertical and horizontal features of the isolator switch, and severe occlusion greatly affects the detection accuracy. To address this issue, the training dataset could be expanded with more images containing occlusions, and more powerful attention mechanisms or context learning techniques could be introduced to enhance the model's focus on local features, thereby reducing the detection errors in occluded situations. Secondly, the issue of viewing angles is also a challenge. When the isolator switch is observed from a side angle, the model's reliance on horizontal and vertical shape features can lead to changes in the switch's appearance, thereby affecting the detection accuracy. To improve the model's performance under different viewing angles, diverse angle data samples could be added, and multi-view learning methods could be incorporated to enhance the model's detection ability across various perspectives. Additionally, small object detection is also a challenge, especially when the isolator switch occupies a very small portion of the image or is captured at a long distance. In such cases, the model often struggles to capture key features, leading to detection failure. This is because small objects have fewer pixels, providing insufficient information for the model. To address this, the efficient-RepGDFPN fusion network could be optimized to enhance the detection capability for small objects, or the image resolution during training could be increased. Additionally, using super-resolution techniques could help to improve the detection accuracy for small objects. Finally, in images with complex backgrounds, such as those containing many wires, transformers, or other electrical equipment, the model occasionally experiences missed detections or false positives.