# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 6 Experimental Results and Discussion
### Sensitivity Analysis of Object Detection Method
#### 6.2.2 Influence of Transfer Learning  
In ship object detection, the transfer learning method is introduced to use the UA-DETRAC high-speed vehicle dataset to reduce the demand for training data for object detection for inland ships. The high-speed dataset consists of 82,085 images with relatively good manual labeling information. The YOLOv5s network is trained using a large number of high-speed vehicle data with specific characteristics similar to the distant images of ships as pre-training weights. Obviously, training can be improved when using UA-DETRAC data pre-training data as training weights. We combine the transfer learning strategy data with ShuffleAttention and ShuffleBackBone, respectively, and compare the detection accuracy of the backbone without the pre-training weight. After using transfer learning, the network has a noticeable improvement in detection performance. The results are shown in Table 2.  
In this study, we utilized a variety of datasets, including ImageNet, COCO, ABOships, Seaships, and UA-DETRAC. These datasets were partitioned into training and validation sets with a 7:3 ratio. The hyperparameters used for training were also kept consistent with those used in the previous experiments. To alleviate the impact of class imbalance, we also introduced the use of image weights during the training process. We conducted training on these datasets for 100 epochs, resulting in the generation of pre-trained weights. Subsequently, we employed these pre-trained weights to perform transfer learning on the ShuffleAttention network architecture proposed in this paper. The transfer learning was conducted on our dataset (consisting of 585 images for training and 1614 images for validation). Finally, we compared the detection performance in terms of the [EMAIL_ADDRESS] value obtained from this transfer learning approach against the baseline results. The results are shown in Table 3. In the comparative analysis, we evaluated the performance of the proposed method by conducting training experiments on our target dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Types** & **Cargo** & **Roll-Roll** & **Container** & **Passenger** & **Buoy** & **Oil** & **Canoeing** & **ALL** \\  & **Ship** & **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 + ShuffleBackBone & 0.804 & **0.678** & 0.796 & 0.697 & 0.664 & 0.728 & 0.851 & 0.745 \\ Original + ShuffleAttention & **0.81** & 0.667 & **0.821** & **0.74** & **0.685** & **0.808** & **0.877** & **0.773** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Algorithm performance comparison of mechanism of attention ([EMAIL_ADDRESS] value).  
In the comparative analysis, we have found that both the COCO and ImageNet datasets exhibit favorable performance in ship detection. These datasets, enriched with numerous maritime-related data, demonstrate impressive results in detecting ships. However, despite their strong performance in specific scenarios, considering the overall [EMAIL_ADDRESS] value, we maintain that employing the UA-DETRAC dataset as the pretrained dataset is better suited for our specific task. On our target dataset, utilizing UA-DETRAC as the pretrained weight yields detection results that align more closely with our requirements.