# A Benchmark for Water Surface Jet Segmentation with MobileHDC Method
## 2 Related Work
### Jet Trajectory Extraction Methods  
According to the different extraction methods, jet extraction can be divided into jet localization based on physical calculation and jet segmentation based on image processing.  
Jet positioning based on physical calculation is based on physical theory and combines the angle and parameters of the jet vice itself to predict the jet trajectory using a formula. [PERSON] [7] et al. established a jet trajectory model based on the angle of the jet and the velocity of the water flow. While this method reduces the error caused by the change in water pressure, it requires the initial striking position and direction of the jet to remain constant, making it unsuitable for complex scenarios when the target is in motion.  
Image-processing-based jet segmentation is achieved by analyzing the shape, color, gray scale, and other features of the jet trajectory, using image detection and segmentation techniques to process the video and images captured by the camera, enabling the extraction of jet trajectories through continuous frame image processing. [PERSON] [8; 9] et al. segmented the jet trajectory by collaborative enhancement of the image and then selecting the threshold value. This method can effectively extract the jet curve with high recognition accuracy, but it requires the position of the camera and the jet apparatus to remain fixed, limiting its flexibility in application.  
To efficiently and accurately extract jet trajectories, image segmentation techniques clearly outperform physical computations. The essence of jet segmentation lies in performing semantic segmentation on jet images. Currently, mainstream efficient semantic segmentation methods are predominantly based on lightweight CNNs. The MobileNet series [10; 11] decomposes standard convolution operations into depthwise separable convolutions and pointwise convolutions, allowing for a significant reduction in parameters while maintaining segmentation accuracy. DFANet [12] introduces a lightweight backbone by modifying the architecture of Xception [13], which is based on depthwise separable convolutions, reducing the input size for faster inference speed. ShuffleSeg [14] utilizes ShuffleNet [15] as its backbone, combining channel shuffling and group convolutions to lower computational costs. BiSeNet [16; 17] proposes a dual-branch architecture to fuse low-level details with high-level contextual information. The Deeplab series [18; 19; 20] employs atrous convolutions with varying dilation rates, expanding the receptive field without compromising spatial resolution. DSNet [21] revisits the application of dilated convolutions, demonstrating that the use of large kernels in dilated convolutions may represent a more powerful paradigm. TopFormer [22] introduces a novel architecture combining CNNs and ViTs, achieving high semantic segmentation accuracy while conserving computational resources. RTFormer [23] effectively leverages global context, improving semantic segmentation through deep attention mechanisms without sacrificing efficiency. SeaFormer [24] introduces an attention mechanism that simultaneously leverages compression-enhanced axial and detail-enhanced features, tailored for semantic segmentation in the mobile computing domain.