## 3 Method
### Environmental Perception Attention (EPA)
Since batch normalization is heavily dependent on the mini-batch size and may have a negative impact on smaller network architectures, we use group normalization to be more stable when training small batch samples or with different batch sizes [47]. Group normalization and the Sigmoid activation function are then used to process the enhanced position information to obtain the representation of position attention in the horizontal and vertical directions. The above process can be expressed as  
\[X^{\prime}=Sigmoid(GN(Conv1D_{H}(Conv1D_{W}(X))))*X \tag{2}\]  
Figure 1: Environmental perception attention (EPA). where \(Conv1D\) represents one-dimensional convolution, \(GN\) represents group normalization, \(Sigmoid\) is a nonlinear activation function, and \(*\) is the element-wise multiplication. In the above process, we only use deep convolution to calculate and fuse features channel by channel, resulting in a lack of cross-channel interaction information. Next, we capture the dependencies and correlations between channels from the information of each channel in the feature map. First, we use global average pooling and global maximum pooling to aggregate and extract representative feature information contained in different channels. Global maximum pooling tends to retain high-frequency information and can capture more salient features such as edges and textures. Global average pooling tends to retain low-frequency information, can capture the overall feature distribution, and pays more attention to low-frequency information. Although second-order pooling such as covariance pooling [48] and bilinear pooling [49] can theoretically capture richer global statistical information, it will bring greater complexity and is not in line with our design concept. Considering that a single method is somewhat rough for representing the global context information of feature maps and may lose a lot of useful information, we combine them to comprehensively describe channel-level statistics. Next, we generate weights for each feature channel from channel-level statistics. Most popular methods adaptively recalibrate channel-wise feature responses by explicitly modeling the interdependencies between channels. SENet [23] fits the complex correlations between channels by connecting two fully connected layers in series, and finally obtains normalized weights through a nonlinear activation function. The two fully connected layers first reduce the dimension and then restore it, which effectively controls the model complexity, but leads to an indirect relationship between the channels and their corresponding weights. In order to reduce the amount of computation, performing dimensionality reduction during the calculation process will damage the direct correspondence between channels and their weights, which will have an adverse effect on the generation of attention weights. Moreover, it is not necessary to capture the dependencies between all channels to achieve better results [31; 50]. Therefore, we introduce a method to avoid dimensionality reduction and local cross-channel interaction in environmental perception attention, which is effective and lightweight. After obtaining the channel information, the channel attention is predicted by aggregating the information between each channel and its k adjacent channels. This operation is achieved by using a one-dimensional convolution of size k. The size of the convolution kernel k determines the coverage of local cross-channel interactions, that is, how many features of adjacent channels will participate in the calculation of the attention weight of a certain channel. The k value affects whether the model tends to have long-range or short-range interactions between channels, and manual adjustment is cumbersome and inefficient in different modules and architectures. Intuitively, the number of input feature map channels is proportional to k, but linear mapping is too simple, so an exponential function can be used to describe the corresponding relationship.