# A Benchmark for Water Surface Jet Segmentation with MobileHDC Method
## 3 Benchmark Description
### Motivation  
As shown in Table 1, public datasets such as COCO, Pascal VOC, and Cityscapes have achieved significant success in object detection, semantic segmentation, and instance segmentation tasks within the computer vision domain. However, these datasets typically focus on fixed entities, such as vehicles, pedestrians, and static objects, and are less comprehensive in covering dynamic or non-fixed phenomena. Moreover, datasets related to water bodies are relatively scarce in the public domain, particularly those that address complex water dynamics and visual characteristics such as ripples, reflections, and variations in visibility. Although datasets like USVInland and WaterScenes involve large-scale water body monitoring, their primary application is in water surface driving scenarios, such as detecting and segmenting obstacles on the water surface; few datasets focus on specialized phenomena such as jet flows. Unlike these datasets, our datasets are characterized by significantly smaller object pixel ratios, accounting for only 1.4% and 2.8% in the Library and SegQinhu datasets, respectively, which greatly increases the difficulty of segmentation. In the absence of a publicly accessible jet segmentation dataset, we have constructed a bespoke one. The aim of our benchmark is, on the one hand, providing high-quality and consistently labeled diverse datasets to facilitate the progress of research in the field of jet segmentation. On the other hand, by focusing on the water scenarios, the benchmark should accurately and efficiently extract the trajectories of the jets, accelerating the development of the field of intelligent jet systems. In order to achieve these goals, our benchmark addresses the general problem of jet semantic segmentation, with the objective of identifying the image regions of small target objects during the training process. In particular, the jet dataset possesses the following characteristics: (i) the morphology of the jet varies depending on the angle and strength of the jet vice strikes, with the jets appearing in the images with non-fixed positions and sizes. Additionally, there is a significant imbalance in the distributions of the pixel classes, which negatively impacts the segmentation accuracy; (ii) the jets are similar in appearance to the background elements, such as water and sky, resulting in blurred edges and susceptibility to noise and background interference. This makes it challenging  
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to accurately distinguish the boundaries of the target from the background; (iii) due to complex lighting conditions, the jet may exhibit strong reflections and highlight regions in certain cases, leading to overexposure in the images. Additionally, reflections on the lake surface and the divergent, mist-like shape of the water column reduce the visibility of the jet target, further complicating the segmentation task. In our segmentation of the jet dataset, we first experimented with the popular large model SAM, anticipating that this pre-trained general model would perform well in the specific context of our dataset. We employed three approaches: automatic segmentation, point prompts, and box prompts. However, the experimental results did not meet expectations, as illustrated in Figure 1. Specifically, the issues are as follows: (i) although the SAM model performs well in common scenarios, it demonstrates limitations in generalization when applied to specific contexts such as the jet dataset. Even with prompts (point prompts and box prompts), the segmentation results did not achieve the desired accuracy; (ii) the coarse-grained nature of the large model results in suboptimal performance for fine-grained segmentation, leading to blurred edges or mis-segmentation, often confusing the jet with the sky or other similar backgrounds, as shown in Figure 1b; (iii) the high computational resource requirements of the large model pose challenges for rapid iteration and deployment in specific tasks.