## 3 DAMP-YOLO Network
### Aggregated Triplet Attention  
In recent years, attention mechanisms have been widely used in various tasks in computer vision. The squeeze-and-excitation (SE) [37] channel attention mechanism learns the importance between channels to improve the useful features and suppress those features that are not very useful for the current task; unlike the SE attention mechanism, efficient channel attention (ECA) [38] does not require channel compression on the input feature map; it uses one-dimensional convolution in the channel dimension to capture the dependencies between channels and extract the importance of the channels; Frequency channel attention (FCA) [39] proves (through mathematics) that global average pooling (GAP) is a particular case of discrete cosine transform (DCT), and the attention mechanism under the frequency domain operation is promoted based on the SE attention mechanism; The convolutional block attention module (CBAM) [40] is a mixed-domain convolutional attention module, which includes a channel attention module and a spatial attention module, the channel attention module can highlight important channels in each feature map, whereas the spatial attention module can focus on the essential regions of the image; Co-ordinate attention (CA) [41] uses co-ordinate information to calculate the attention weight so that the neural network pays more attention to the spatial position relationship between different pixels; triplet attention [42] uses a three-branch structure to capture crossdimensional interactions to calculate attention weights, establish spatial and channel dependencies through rotation operations and residual transformations, and encode interchannel and spatial information with negligible computational overhead. We compare several different attention mechanisms. As shown in Table 1, the triplet attention mechanism performed best after adding it to the yolov8n network. So, we finally made improvements based on the triplet attention mechanism to improve the network's performance. Triplet attention is an attention mechanism with a three-branch structure. The main idea is to divide the input feature representation into three branches; each branch is responsible for calculating and applying the attention weights of two dimensions in the three dimensions of the input tensor to capture the crossdimensional interaction information between different dimensions. The structure diagram of triplet attention is shown in Figure 7. The first branch captures the crossdimensional interaction information between the two dimensions of channel C and space height, H; the second branch captures the  
Figure 6: Architecture of deformable CSP bottleneck (DCB) module, which improves the feature extraction ability. interaction information between channel C and the spatial width, W; the third branch captures the interaction information between spatial height, H, and width, W. Specifically, the input tensor passes through the average pooling and maximum pooling layers of Z-Pool (zeroth pool), one of the three dimensions is compressed and then calculated by the convolutional layer and activated by Sigmoid to become an attention weight in the range of 0-1, which is subsequently multiplied by the original input. Finally, the outputs aggregate the three branches by taking the mean value.