# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 6 Experimental Results and Discussion
### Comparison with Other Papers  
This section compares the proposed multi-object detection and tracking framework with popular multi-type object ship detection papers. The key index is the amount and type of data they use with the value of [EMAIL_ADDRESS].  
Paper [1], using YOLOv4-tiny and YOLOv4-tiny-3l for ship object detection, xxx in yolov4-tiny-xxx or yolov4-tiny-3l-xxx in the table below, represents the input image size. The author uses the SeaShips dataset [43] and the ABOships dataset [44]. The two datasets are divided into 70 percent training and 30 percent validation sets. The SeaShips dataset contains 31,455 images taken from video segments acquired from coastline video surveillance systems. The ABOships dataset contains 9,880 images. ABOships dataset images, on the other hand, were obtained through a camera mounted on a ferry, providing footage from the point of view of the vehicle and providing annotations for nine types of vessels, seamarks, and miscellaneous floaters. ABOships datasets contain many small objects, similar to the small buoys in our dataset. Too many small objects will cause the [EMAIL_ADDRESS] value of the overall dataset to decrease significantly. In ref. [45], an enhanced CNN-enabled learning method was proposed. Combined with yolov3, the accuracy of ship multi-type object detection is improved. The dataset used in this article is 7000 from SeaShips.  
The original YOLOv5s network and our proposed framework (transfer learning + improved YOLOv5s network) were employed to train and evaluate their performance on the SeaShips (7000) and ABOships (9880) datasets, essential datasets in the maritime domain. To ensure a comprehensive evaluation, we adopted two distinct data partitioning strategies for training. The first approach adhered to the standard convention used in related works, where the ABOships and SeaShips datasets were partitioned into a 70% training set and a 30% validation set. Subsequently, we employed the second approach to investigate the efficacy of transfer learning with a limited training set. Specifically, we utilized a mere 10% of the data from each dataset for training, reserving the remaining 90% for validation purposes. This approach facilitated a robust comparison between the results of our proposed framework and other existing approaches cited in the literature. Through meticulous analysis and comparison of the final [EMAIL_ADDRESS] values, our proposed method, which integrates transfer learning with our improved YOLOv5s-based network, demonstrated expedienced proficiency in effectively training on both the ABOships and SeaShips datasets. Notably, our proposed framework achieved high levels of accuracy and recall on the validation sets. These results not only showcase the superiority of our proposed framework but also highlight its capability to address the challenges presented by these specific datasets.  
By incorporating the two metrics, FPS and params, introduced in Table 6 of Section 6.3, these metrics have also been included in the table below for our data comparison. From the comparative data, it can be observed that our model achieves a high detection [EMAIL_ADDRESS] while simultaneously demonstrating faster detection speeds and smaller model parameters.  
The results are shown in Table 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Pretrained Datasets** & **FPS** & **Params** \\ \hline Faster-RCNN [1] & 3.75 & 136,811,934 \\ Yolov3 [38] & 82.6 & 61,529,740 \\ Yolov4-tiny [39] & 409.22 & 5,887,976 \\ Yolov4 [40] & 66.42 & 6,042,3500 \\ Yolov5s [41] & 144.9 & 7,029,004 \\ Yolov7 [42] & 120.5 & 37,227,020 \\ Yolov8s [41] & 417.7 & 11,138,309 \\ Proposed framework & 128.2 & 7,029,532 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Algorithm performance comparison with FPS and Params.