## 4 Results
### Comparison Experiment  
To validate the performance of the proposed EMB-YOLO model in the task of isolation switch detection, we conducted a series of comparative experiments with various object detection models. Specifically, EMB-YOLO was compared with transformer-based object detection algorithms, such as RT-DETR [41]. Additionally, it was evaluated against single-stage object detection algorithms including YOLOv5, YOLO8, YOLOX-tiny [46], and YOLOv10 [47]. Furthermore, we performed comparative experiments using the two-stage object detection algorithm faster-RCNN [9]. The results of these comparative experiments on our custom isolation switch status dataset were as follows:  
As shown in the Tables 1 and 2, the proposed EMB-YOLO model outperformed the baseline model YOLOv8n of similar scale, with [EMAIL_ADDRESS] and mAP@5:0.95 improving by 3.6% and 2.2%, respectively, while also reducing the computational complexity and parameter count. Compared to several well-known algorithms, such as RT-DETR-L (80.8), YOLOv5n (82.1), Faster-RCNN (84.2), and YOLOv10 (72.3), our EMB-YOLO achieved a significant improvement in [EMAIL_ADDRESS], increasing by 6.4%, 5.1%, 3%, and 14.9%, respectively. Additionally, EMB-YOLO demonstrated reductions in both parameter count and computational cost when compared with RT-DETR-L, YOLOv5s, YOLOv8, YOLOX-tiny, and faster-RCNN. Our EMB-YOLO achieved the highest F1 score, indicating that it strikes a good balance between precision and recall, resulting in a more stable performance. In terms of real-time capabilities, it was comparable to YOLOv5s and YOLOv8s, meeting the requirements for real-time detection. This performance advantage can be attributed to the use of the EMBC module in EMB-YOLO, which enhances positional information in both horizontal and vertical directions, captures long-range dependencies more effectively, and strengthens the model's ability to detect isolation switch features, while reducing the number of parameters and computational complexity. This highlights the model's outstanding lightweight performance.  
To validate the effectiveness of the ELA attention module used in the EMB-YOLO detection model, we conducted experiments by replacing the ELA attention mechanism within the C2f module. Various comparative experiments were performed to demonstrate the superiority of the ELA attention module in EMB-YOLO. The results, as shown in Table 3, indicated that the model incorporating the ELA attention module in the C2f module achieved a better performance for both [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 on the self-constructed isolation switch dataset. Specifically, compared to the CA attention, SE attention, CBAM attention, and EMA attention modules, the [EMAIL_ADDRESS] improved by 0.3%, 5.2%, 3.7%, and 0.4%, respectively. Additionally, in terms of model complexity, the computational load decreased compared to the EMA and CA attention modules, and the FPS reached 38.4, outperforming the CA attention, SE attention, and EMA attention modules.  
We further visualized the training weights of the detection models by implementing five different attention modules (CA, SE, ECA, CBAM, ELA) on the custom isolation switch state detection dataset. The visualization results are presented in Figure 7. It can be observed that, compared to other attention mechanisms, the ELA attention mechanism employed in this study allowed the model to focus more intensively on the elongated features of the isolation switch. This better concentrated and sensitive focus significantly aided the detection model in accurately identifying and localizing the isolation switch, thereby enhancing the model's precision in detecting isolation switch objects.