###### Abstract  
With the continuous progress of intelligent power system technology, in order to meet the needs of substation operation and maintenance, a target detection algorithm is applied to identify the status of equipment switches. YOLOv7, as the latest achievement of YOLO (You Only Look Once) series algorithms, has good speed and accuracy in target detection tasks. However, when the generalized network is applied in a specific scenario, its advantages are not obvious due to its high weight and poor portability. In this paper, an improved GF-YOLOv7 network model is proposed to apply in the recognition of the status of bounce locks in a substation. The MobileViT module is used to improve the feature extraction ability of the backbone network. Referring to the CBAM feature attention mechanism, the channel attention module and the spatial attention module are used to design a more lightweight feature fusion network. The experimental results in the test set show that the proposed network can significantly reduce the network weight and improve the detection accuracy on the basis of a small reduction in the detection speed, and the accuracy reaches 97.8%, which can meet the needs of the detection task of substation bounce locks. substation bounce lock; YOLOv7; attention mechanism; target detection +
Footnote †: journal: Applied sciences  
Article An Improved YOLOv7 Model Based on Visual Attention Fusion: Application to the Recognition of Bouncing Locks in Substation Power Cabinets  
YOLOsubstation switchgear status recognition method based on the YOLOv3 network. Therefore, the detection of electrical equipment lock status is of extreme importance to ensure the safety and reliability of substation equipment through preventive maintenance. The target detection algorithm is an important part of computer vision. It can obtain image features by calculating the weight parameters to discriminate the location and type of the target to be detected in the image. Common target detection models are region-based CNN (Convolutional Neural Network) [10; 11], SSD (Single Shot Detector) series [12], YOLO (You Only Look Once) series [13; 14], DETR (Detection Transformer) series [15], and their derived networks [16; 17]. The two-stage algorithm represented by R-CNN and its derived network generally performs the target detection process as follows: (1) extract possible areas with targets by using a filtering algorithm; (2) obtain features by the CNN operation to determine the presence and category of targets in the candidate area; (3) calculate the coordinates of the position frame of the returned object by convolution to improve the positioning accuracy. Fast R-CNN changes the way R-CNN extracts the related features of each candidate box from one operation; it then scales each area of the feature map using the pooled features of the area of interest, and finally classifies and regresses the features and locations of the target [18]. [PERSON] et al. [19] studied the generation and classification of alternative windows together with regression and proposed the Faster R-CNN network. In addition, there are two-stage target detection networks, such as CoupleNet [20], Cascade R-CNN [21], and other derived networks based on image pyramids and feature pyramids. The YOLO series of algorithms is the most widely used one-stage target detection algorithm at present. [PERSON] et al. proposed the initial YOLO [13] model based on estimating the existence probability, types, and position deviation of objects in each detection area. On this basis, the batch normalization, high-resolution input, and full convolution operations are introduced to improve the target detection accuracy to achieve YOLOv2 [22]. YOLOv3 [14] uses Darknet53 as the backbone network and incorporates multiscale prediction improvements. YOLOv4 and YOLOv5 are mainly improved on data preprocessing and feature fusion [23]. YOLOv5 is designed to adapt to different target detection tasks by scaling the width and depth equally. YOLOv6 and YOLOv7 were derived from the YOLO series in 2022.