# Fast 3D Transmission Tower Detection Based on Virtual Views
## 3 Methodology
### Local-Global Dual-Path Feature Fusion Network  
To address the challenge of detecting TTs in virtual views, where they are relatively small compared to the whole scene and inherently difficult to detect, this paper refines the feature extraction architecture. Inspired by the principles of visual focus and attention, and incorporating multi-scale feature fusion [27], we developed a local-global dual-path feature fusion network based on the YOLO framework [24] for TT detection.  
The whole algorithm is divided into three parts: the input part simulates the amplification of visual focus in images, the backbone part performs feature extraction of the local and global dual paths and fuses feature maps at different scales, and the head part realizes the inference prediction of objects. Figure 4 illustrates the overall structure of the object detection network.  
The algorithm model performs sub-block partitioning of the input image at the input end, taking into account both computational speed and accuracy, and divides the image into four sub-blocks. Subsequently, it enriches the feature information of weak targets by locally magnifying the visually attended regions of the partitioned image. The magnified regions are then input through the branch streams for feature extraction and combined with  
Figure 3: Illustration of a portion of a generated virtual view.  
the globally extracted features from the virtual view for detection. The globally extracted features are primarily used for background detection, while the locally extracted features are used for detecting TTs. The design that significantly enhances the local area serves as an extension of the visual attention mechanism, amplifying the overall and detailed information of the target object of interest, while enhancing its spatial location information, making the model more sensitive to small-sized TTs. Additionally, the feature extraction module fuses local and global feature information from different branches at multiple scales, which can more effectively obtain precise location information and high-level semantic information, increasing spatial correlation. Unlike existing attention mechanism modules, the dual-path feature fusion network magnifies the focal area at the data input end and expands the spatial information of the target through multi-scale feature information fusion in dual branches, thereby altering the weight of the feature maps.  
The branch input for the local area passes through the convolutional layers of the backbone and fuses features with the convolutional layers of the global area input at multiple scales. This is achieved by using a Concat (channel connection) layer to connect the channels and then passing through a \(1\times 1\) convolutional layer to reorganize the feature channels and update the attention weights for small-sized target areas, removing redundant information. The feature extraction architecture for the branch input is similar to that for the global area input. The feature maps of the branch local input are combined with the global feature map size and the sub-block coordinate information at the input for interpolation and completion, followed by a Concat layer and convolutional modules that merge with the global feature maps. Since the output of the Concat layer realizes the connection of local and global input channel data, the algorithm places a \(1\times 1\) convolutional layer after the Concat layer to integrate feature map information of targets and backgrounds, with a SiLU activation function layer following each convolution. In each branch's feature extraction stage, a \(3\times 3\) convolutional module with a stride of 2 is used to downsample the feature maps, which are then passed to the Concat layer along with other inputs, forming a multi-scale feature fusion. The resulting backbone feature maps contain the largest receptive field, with the richest semantic and spatial information.  
Figure 4: Local–global dual-path feature fusion network.