## 3 Materials and Methods
### Tibia-YOLO Network Structure
#### 3.2.3 EMA Module  
[PERSON] et al. [12] noticed that the ParNet module used parallel subnetworks to improve the efficiency of feature extraction. Triplet attention involves a triple parallel-branch structure that can capture the cross-latitude effects of different parallel branches. Coordinate attention (CA) [35] combines information in specific directions. Embedding into the same spatial dimension allowed good performance improvements to be achieved. Inspired by these attention mechanisms, [PERSON] et al. [12] changed the sequential processing method for CA and designed a multi-scale parallel subnetwork. In order to avoid the dimensionality reduction caused by having convolutional networks in the network, the dimensions of some channels were reshaped into batch dimensions. The \(1\times 1\) convolution module in the original CA module was taken out as a shared component and named the \(1\times 1\) branch in the EMA network. In order to aggregate multi-scale information, a \(3\times 3\) kernel and \(1\times 1\) branch were designed and arranged in parallel, thus establishing effective short and long ranges. In order to enable neurons to collect multi-scale spatial information, three parallel pathways were set up in the EMA network, two of which were \(1\times 1\) branches from the CA module, and two 1D global average pools were used for the channels in the spatial direction. There was also a \(3\times 3\) branch in which only a \(3\times 3\) kernel was used to capture multi-scale feature representations. The network structure of the EMA module can be seen in Figure 6. Finally, a novel efficient multi-scale attention (EMA) module was proposed. In comparison with the convolutional block attention module (CBAM) [36], efficient channel attention (ECA) [37], shuffle attention (SA) [38], and normalization-based attention module (NAM) [39], EMA achieved better results. In the past, many attention mechanisms have been widely used, but these mechanisms have certain limitations, for instance, triplet attention enhanced feature representation in three different dimensions, but there are limitations when processing features of different scales. Coordinate attention (CA) focuses on capturing spatial information, and the attention mechanism is enhanced through position information. However, the ability to capture complex spatial information is insufficient, and a single CA mechanism cannot capture all key information. Channel attention focuses on processing the relationships between channels and uses the enhancement of important channels to strengthen feature representations, but it ignores the performance of features at different scales. Efficient channel attention (ECA) is a lightweight channel attention mechanism, but it is not good at handling the complexity of spatial information. Figure 5: Diagram of the ParNet module’s structure. Unlike separate attention mechanisms, EMA was designed to consider a variety of features from small to large scales, and it can perform better in complex detection environments. It solves the common problems of current attention mechanisms, such as the insufficient comprehensiveness of feature expression and insufficient ability to process features of different scales. At the same time, compared with other attention mechanisms that have too many parameters, EMA ensured efficiency while improving accuracy in the network, thus effectively saving computing resources, and it has been widely used in various scenarios, such as deployment in meat and poultry breeding environments and in portable agricultural equipment. Therefore, the EMA could become an effective tool in many complex recognition tasks. EMA's multi-scale attention can help increase the generalization capabilities of model learning and cope with some abnormal data, such as the rapid breathing of broilers during DR imaging, which causes image deviation. Considering the particularity of the broiler tibia detection task, the target area was a large target, and there was a certain degree of similarity between datasets.