# A Efficient and Accurate UAV Detection Method Based on YOLOv5s
## 3 Materials and Methods  
### Lightweight Feature Extraction Module  
When deploying network models to actual devices, researchers have observed significant variations in inference speeds among models with similar floating-point operations (FLOPs). This highlights that memory access times have a substantial impact on inference speed, an effect that cannot be assessed solely by FLOPs. To address this, we have employed the ShuffleNetV2 [37] to replace the backbone of YOLOv5s, incorporating the Coordinate Attention (CA) mechanism [44] to efficiently utilize limited computational resources and achieve an ideal balance between speed and accuracy. ShuffleNetV2 has undergone comprehensive enhancements in efficient network design. Initially, it avoids complex branching structures by employing a Channel Split operation, which divides the feature map into two branches along the channel dimension. One branch performs an identity mapping, while the other undergoes three convolutions, including two \(1\times 1\) standard convolutions and a central depthwise separable convolution. These branches are then concatenated along the channel dimension, ensuring the number of channels in the input and output feature maps remains equal. This process avoids element-wise additions, significantly reducing memory access times. Lastly, a Random Shuffle operation ensures thorough information exchange among channels. ShuffleNetV2 has optimized its downsampling module. During downsampling, to increase the total output channel count, the initial Channel Split operation is omitted. Instead, the information is processed separately and then concatenated, effectively doubling the output channels. Additionally, a \(3\times 3\) average pooling layer in one branch is replaced with a \(3\times 3\) depthwise separable convolution to enhance feature extraction capabilities. The structure of ShuffleNetV2 is shown in Figure 2.  
The CA mechanism is widely recognized as a key technique for enhancing feature expression capabilities in mobile networks [45; 46]. This mechanism decomposes channel attention into a pair of one-dimensional feature encodings that aggregate features along different directions, effectively capturing cross-channel information and perceiving directional and positional information to focus attention on regions of interest. The structure of the coordinate attention mechanism includes the following steps. As illustrated in Figure 3, upon receiving the input feature tensor x, it first encodes all channels along the horizontal and vertical directions using pooling kernels of sizes (H,1) and (W,1) respectively, to extract directionally-aware feature information. These transformations  
Figure 2: (a) The basic unit of ShuffleNetV2; (b) The spatial down sampling unit of ShuffleNetV2. enable the network to learn interactions between features over long distances and their coordinate information, thus improving target localization accuracy. \[Z_{c}^{h}(h)=\frac{1}{W}\sum\limits_{0\leq i<W}X_{c}(h,i) \tag{1}\]  
\[Z_{c}^{w}(w)=\frac{1}{H}\sum\limits_{0\leq j<H}X_{c}(j,w) \tag{2}\]  
The aggregated feature maps are initially concatenated and then processed through a \(1\times 1\) convolutional function \(\mathbf{F}_{1}\):  
\[f=\delta\Big{(}F_{1}\Big{(}[z^{h},z^{w}]\Big{)}\Big{)} \tag{3}\]  
Here, \([\,\ ]\) denotes the concatenation of feature information, \(\delta\) represents the activation function, and \(\mathbf{f}\in\mathbf{R}^{\frac{C}{r\cdot(H+W)}}\) is an intermediate feature map that encodes spatial information in both horizontal and vertical directions. The parameter \(\mathbf{r}\) controls the scale of the linear transformation.