# A Benchmark for Water Surface Jet Segmentation with MobileHDC Method
## 3 Benchmark Description
### Benchmark and Datasets  
We now present a proposed benchmark and two datasets built for jet image segmentation in detail. Generally speaking, there are three main ways to collect jet image datasets in practical applications: electro-optical acquisition, camera acquisition, and mobile phone acquisition. The datasets presented in this paper were all collected using an electro-optical gimbal. The primary advantage of using electro-optical gimbal is their ability to capture high-quality, stable images even in dynamic environments. Table 2 below provides a detailed breakdown of the number of images collected under various challenging conditions. Figure 1: Segmentation performance of the SAM model on the Library and SegQinhu datasets, revealing issues in segmentation where the model tends to misclassify background as jet flow. Library. The images in the Library dataset were acquired in a relatively closed-pool environment: a small, manually constructed test cell for jet experiments. It contains 1300 pixel-level annotated image data. Due to the limitation of the site size, the camera has a restricted viewing angle when acquiring the images, and the jet vice has a limited range when performing target strikes, resulting in the jet dataset exhibiting a significant pixel class imbalance. The distribution of the jet sizes is shown in Figure 2a. The background pixels make up the vast majority of the image, whereas the coverage of the pixels of true jet trajectory is just less than 5% of the entire image, with most of them concentrating in the range of 0.5% to 2%. Figure 3 illustrates the typical characteristics of the dataset, including images captured from various viewpoints and the appearance of the jet under conditions of strong lighting or minimal pixel coverage. When exposed to strong lighting, the jet stream appears brighter and lighter, reducing contrast with the background. This issue is particularly pronounced against visually similar backgrounds, such as white walls, as shown in Figure 3b, where the distinction between the jet and the background is significantly diminished, greatly increasing the difficulty of jet trajectory extraction. \begin{table}
\begin{tabular}{c c c} \hline
**Challenging Scenarios** & **Library** & **SegQinhu** \\ \hline Strong light exposure & 373 (28.69\%) & 125 (15.19\%) \\ Occlusion or interference & 79 (6.08\%) & 84 (10.21\%) \\ Minimal pixel coverage & 59 (4.54\%) & 17 (2.07\%) \\ Multi-angle capture & 138 (10.62\%) & 19 (2.31\%) \\ \hline \end{tabular}
\end{table} 
Table 2: The number of images in challenging scenarios from the Library and SegQinhu datasets. The minimal pixel coverage specifically refers to images containing only the impact points or partial water columns. Figure 2: Relative frequency of annotated jet pixels within an image over the 1300 images in the Library dataset (**a**) and the 823 images in the SegQinhu dataset (**b**), respectively. Here, the fraction of jet pixels serves as proxy for the size of the objects of interest within an image. (**a**) Library, (**b**) SegQinhu. SegQinhu. The dataset was taken in an open natural lake scene. It consists of 823 images with pixel-level annotations, where the proportion of jet pixels in all images does not exceed 10%, with the majority concentrated in the 1% to 4% range. The distribution of the jet sizes is shown in Figure 1(b). Considering the complexity of real-life scenarios and to ensure the diversity of the data, we collected the images at different times under different weather conditions, such as sunny, overcast, and cloudy, and at 10:00, 13:00, 15:00, and 17:00 on the same day. Compared with the Libary dataset, the SegQinhu dataset contains different light conditions and weather conditions that cover a wider range of scenarios. This makes it more representative of the actual operating conditions of intelligent jet systems in complex scenarios, and thus facilitates the training of more robust models.