# DMS-YOLOv5: A Decoupled Multi-Scale YOLOv5 Method for Small Object Detection
## 4 Experiments
### Experimental Results and Analysis
#### 4.3.6 Ablation Experiments and Results Analysis  
Based on the YOLOv5 original model, we improved the spatial pyramid pooling module in the feature extraction network, the attention mechanism before and after the feature fusion network, and the detection head network. By combining the optimal models from each part of the previous experiments, we proposed the DMS-YOLOv5 model. We compared the performance of the DMS-YOLOv5 model with the original model and other models. The results of the comparison are shown in Table 6, where Method 1 to Method 5 represent each model improvement. The mean average precision of the DMS-YOLOv5 model improved by 12.1% compared to the original model. The precision has improved by 10% compared to the original model, leading to a significant enhancement in the recognition ability for various objects. The recall rate has also increased by 17.4% compared to the original model, reducing the occurrence of missed detections for different types of objects. The comparison of the three metrics, precision, recall, and mean average precision, can be clearly seen in Figure 13.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **RFB** & **CA** & **4P** & **Decoupled** & **Loss** & **mAP\_0.5** & **Precision** & **Recall** \\ \hline YOLOv5 & & & & & & 27.6 & 38.2 & 29.5 \\
1 & \(\surd\) & & & & & 28.8 & 38.1 & 31.3 \\
2 & \(\surd\) & \(\surd\) & & & & 32.2 & 40.3 & 35.9 \\
3 & \(\surd\) & \(\surd\) & \(\surd\) & & & 35.7 & 42.5 & 39.1 \\
4 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & & 38.9 & 45.6 & 44.4 \\
5 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 39.7 & 48.2 & 46.9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of ablation experiment results on the dataset (%).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **mAP\_0.5** & **Precision** & **Recall** \\ \hline YOLOv5 & 27.6 & 38.2 & 29.5 \\ YOLOv5\_DIoU & 28.8 & 39.1 & 29.9 \\ YOLOv5\_CIoU & 29.4 & 39.0 & 30.2 \\ YOLOv5\_CIoU\_D-NMS & 31.8 & 40.2 & 37.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of the improved loss function and NMS algorithm on the datasets (%).  
After the improvement and analysis of each level of the network, we summarized the following rules:  
1. The multi-scale receptive field in the feature extraction network can be used to achieve feature extraction tasks for different scale objects, especially for small object datasets, where multi-scale receptive field can give more attention to low-resolution small objects.
2. The introduction of the channel and spatial attention CA mechanism further reduces the parameter and computational complexity of the attention mechanism.
3. The introduction of the small object detection layer solves the problem of the loss of detailed information of low-resolution small objects due to the high sampling multiple in the downsampling process.
4. The decoupled network is introduced into the detection head network to achieve branch processing for classification and boundary box regression tasks, improving the detection accuracy and efficiency of the two tasks.
5. Improving the boundary box loss function and NMS algorithm can alleviate the problem of missed detection caused by the concentration of small objects and the mutual occlusion between objects.  
In summary, the DMS-YOLOv5 model has shown good performance in mean average precision, precision, and recall.