## 3 Experimental Test and Result Analysis
### Experimental Test
#### 3.4.3 Comparative Experiment  
The specific indicators for evaluating the performance of the algorithm in this paper include precision (\(P\)), recall (\(R\)), and mAP, where mAP @ 0.5 represents the average precision mean when the intersection over the Unio IOU (IOU) threshold is 50% and mAP0.5-0.95 represents the average precision mean of the IOU threshold in the range from 50% to 95%. This process can be described as follows:  
\[P=\frac{TP}{TP+FP} \tag{4}\]  
\[R=\frac{TP}{TP+FN} \tag{5}\]  
\[\text{mAP}=\frac{\sum_{k=1}^{n}PR}{N} \tag{6}\]  
In the formula, \(TP\) represents the number of correct positive samples, \(FP\) represents the number of wrong positive samples, \(FN\) represents the number of wrong negative samples, and \(N\) represents the number of types in the sample. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Environment Configuration** & **Name** & **Related Configuration** \\ \hline \multirow{3}{*}{Hardware environment} & CPU & Intel(R) Core (TM) i7-7700 HQ [EMAIL_ADDRESS] GHz \\  & Running memory & 8 G \\  & GPU & NVIDIA GeForce GTX 1050 Ti \\ \hline \multirow{3}{*}{Software environment} & Operating system & Windows10 \\  & Python & 3.8 \\ \cline{1-1}  & Deep learning framework & Pytorch \\ \cline{1-1}  & CUDA & 11.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental environment. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Parameter Description** & **Value** \\ \hline lr0 & Initial learning rate & 0.001 \\ lrf & cyclical learning rates & 0.2 \\ weight\_decay & Weight attenuation parameter is used to prevent model over-fitting & 0.0005 \\ warmup\_epochs & Warmup learning rounds & 3.0 \\ momentum & Warmup learning momentum & 0.8 \\ warmup\_bias\_lr & Warmup learning rate & 0.1 \\ IoU loss coefficient & It is used to measure the overlap between the predicted bounding box and the real bounding box & 0.05 \\ cls loss coefficient & It is used to measure the prediction accuracy of the model for the target category & 0.5 \\ cls BECloss & Positive sample weight & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameter settings. Params are an important indicator for measuring model complexity. The more parameters a model has, the more computing resources and data it requires for training and inference. GFLOPS stands for the computational efficiency and speed of a model. It denotes the number of floating-point operations needed to run a network model once. It measures the number of floating-point operations a model performs during a forward propagation. In order to verify the effectiveness of the improvement of the attention mechanism, the added attention mechanism is replaced, including squeeze-and-excitation (SE) [27], the convolutional block attention module (CBAM) [28], efficient channel attention (ECA) [29], coordinate attention (CA) [30], and EMA. The effects of different attention mechanisms on the model detection effect are compared and analyzed. The \"-\" indicates that the attention mechanism is not applied to the model. The same parameters are used in the training process, and experiments are performed on the VOC dataset. The results are shown in Table 3.  
SE is a classic channel attention mechanism, which strengthens the importance of feature channels by compressing and stimulating processes. As another form of channel attention, ECA enhances feature representation by effectively capturing cross-channel correlations but ignores spatial location information. CA integrates location information into channel attention and processes features in different spatial directions through two feature coding steps, thereby generating weights that fuse channel and spatial information. CBAM combines the advantages of channel and spatial attention mechanisms and models the channel and spatial weights independently, which not only strengthens the relationship between channels but also considers the spatial interaction and realizes the comprehensive optimization of features.