# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 6 Experimental Results and Discussion
### Setup  
To validate the superior performance of the object detection network, we conduct a series of experiments on our dataset. We introduce other state-of-the-art (SOTA) object detection models and train and validate them on our dataset. These competing methods use the same training and validation sets and are trained for 130 epochs with original pretrain weights. We compare the method (ShuffleAttention + transferlearning) with other competing methods on our test datasets (145 images). The specific comparative data are shown in Table 5. The proposed framework shows the best detection performance for all types of objects except the oil tanker. Still, for oil takers, the [EMAIL_ADDRESS] value of the proposed framework is the second. It is worth noting that we have also included two additional metrics, FPS and params, to evaluate our model's performance. FPS refers to the number of frames per second that the model can process during image or video inference (object detection and localization); it can be influenced by various factors, such as the resolution of input images, model complexity, and the availability of computational resources. In this section, FPS calculations for the experiments were consistently conducted on the NVIDIA GeForce RTX 3090, with validation performed on our test datasets consisting of 145 images. The size of params indicates the model's parameter count, which is influenced by both the network architecture and the number of classes in the training dataset (nc). The size of 'params' reflects the complexity of the model. The results are shown in Table 6. The results indicate that our proposed framework achieves the highest level of accuracy on the test dataset while maintaining faster detection speeds.  
\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 + lanchor & 0.719 & 0.63 & 0.683 & 0.614 & 0.687 & 0.566 & 0.79 & 0.67 \\ Shuffleonlybackbone + lanchor & 0.737 & 0.561 & 0.694 & 0.609 & 0.692 & 0.619 & 0.796 & 0.672 \\ Shuffleattention + lanchor & 0.72 & 0.611 & 0.657 & 0.589 & 0.721 & 0.584 & 0.809 & 0.67 \\ ShuffleBackbone + transferlearning + lanchor & 0.811 & 0.718 & 0.795 & 0.776 & 0.813 & 0.768 & 0.808 & 0.784 \\ ShuffleAttention + transferlearning + lanchor & **0.827** & **0.739** & 0.813 & **0.785** & **0.813** & **0.78** & 0.818 & **0.796** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Algorithm performance comparison of smaller anchor box ([EMAIL_ADDRESS] value).  
\begin{table}
\begin{tabular}{c 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 Faster-RCNN [3] & 0.73 & 0.749 & 0.813 & 0.702 & 0.28 & 0.861 & 0.683 & 0.688 \\ Yolov3 [38] & 0.636 & 0.4 & 0.673 & 0.513 & 0.496 & 0.638 & 0.226 & 0.51 \\ Yolov4-tiny [39] & 0.76 & 0.705 & 0.836 & 0.835 & 0.431 & 0.796 & 0.538 & 0.70 \\ Yolov4 [40] & 0.787 & 0.833 & 0.845 & 0.838 & 0.639 & **0.952** & 0.565 & 0.778 \\ Yolov5s [41] & 0.809 & 0.737 & 0.821 & 0.718 & 0.675 & 0.753 & 0.726 & 0.749 \\ Yolov7 [42] & **0.914** & 0.575 & **0.923** & 0.803 & 0.818 & **0.894** & 0.613 & 0.791 \\ Yolov8s [41] & 0.869 & 0.688 & 0.849 & 0.782 & 0.724 & 0.827 & 0.658 & 0.771 \\ Proposed framework & 0.866 & **0.995** & 0.851 & **0.925** & **0.819** & 0.878 & **0.995** & **0.904** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Algorithm performance comparison with SOTA methods ([EMAIL_ADDRESS] value).