# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 6 Experimental Results and Discussion
### Sensitivity Analysis of Object Detection Method
#### 6.2.3 Influence of Smaller Anchor Box  
In object detection, we introduce a smaller anchor box, which uses the feature map of 160 \(\times\) 160 to improve the detection of small objects. The data reflect the improvement of the detection accuracy of the buoy. However, adding the detection box for small targets may decrease the detection accuracy and recall rate of large ship targets. We conduct experiments on the original yolo network, the network adding a small object detection layer, the network adding ShuffleBackBone and the small object detection network attention mechanism, and the network adding ShuffleAttention and small object detection attention mechanism, respectively. The results are shown in Table 4.  
The detection capability of the network for the small target buoy is improved to a certain extent. However, the accuracy of other categories and the overall detection network is decreased. When small object detection (+1 anchor) is added to the strategy (ShuffleBackBone + transferlearning) that we think can obtain the best detection result, the overall [EMAIL_ADDRESS] value will decrease. However, the detection rate of the buoy increased. Thus, a network without small object detection would work better. However, for specific small object detection tasks, such as buoy detection, we can try adding small object detection into the network to improve detection accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Types** & **Cargo** & **Roll-Roll** & **Container** & **Passenger** & **Buoy** & **Oil** & & \\  & **Ship** & **Ship** & **Ship** & **Ship** & **Tanker** & **Canoeing** & **ALL** \\ \hline Original & 0.776 & 0.647 & 0.814 & 0.671 & 0.638 & 0.757 & 0.836 & 0.734 \\ Original + transfer learning & 0.871 & 0.737 & **0.856** & **0.835** & 0.792 & 0.801 & 0.73 & 0.803 \\ ShuffleBackBone + transferlearning & 0.873 & 0.721 & 0.854 & 0.82 & 0.783 & 0.865 & 0.863 & 0.826 \\ ShuffleAttention + transferlearning & **0.885** & **0.753** & 0.854 & 0.833 & **0.794** & **0.869** & **0.957** & **0.849** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Algorithm performance comparison of transfer learning ([EMAIL_ADDRESS] value).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Pretrained Datasets** & **Cargo** & **Roll-Roll** & **Container** & **Passenger** & **Buoy** & **Oil** & **Canoeing** & **ALL** \\  & **Ship** & **Ship** & **Ship** & **Ship** & **Ship** & **Tanker** & **Canoeing** & **ALL** \\ \hline ImageNet & **0.894** & 0.738 & 0.886 & 0.841 & 0.767 & 0.849 & 0.651 & 0.803 \\ COCO & 0.885 & **0.797** & **0.887** & **0.854** & 0.778 & 0.836 & 0.77 & 0.829 \\ ABOships & 0.85 & 0.717 & 0.831 & 0.773 & 0.761 & 0.793 & 0.745 & 0.781 \\ Seaships & 0.884 & 0.712 & 0.864 & 0.796 & 0.744 & 0.825 & 0.697 & 0.789 \\ UA-DETRAC & 0.885 & 0.753 & 0.854 & 0.833 & **0.794** & **0.869** & **0.957** & **0.849** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of mAP values of different pretrained datasets and the proposed dataset ([EMAIL_ADDRESS] value).