# A Benchmark for Water Surface Jet Segmentation with MobileHDC Method
## 1 Introduction  
Intelligent jet systems play a crucial role in various industries and applications, from firefighting to marine and underwater operations. These systems are employed in environments ranging from land to sea, with usage in engines, sprayers, water cannons, and underwater vehicles. As technology continues to advance, intelligent suction and jet equipment has undergone significant technical evolution. In recent years, the development of various suction and jet devices has been predominantly driven by trends toward intelligent systems and automation. In firefighting, for instance, traditional fire extinguishing systems primarily operating through high-pressure pumps to generate powerful water jets are used for firefighting and emergency response. However, these systems often fall short of achieving optimal efficiency, ease of use, and reliability, and even pose inherent risks in some cases [1]. Consequently, enhancing the precision of intelligent jet system targeting and theautomation of jet control has become a focal point of research. In practical applications, the rapid identification and localization of targets, as well as the precise trajectory extraction of the water jet, are critical to ensuring the effectiveness of intelligent jet systems. In this paper, we take a step toward computer-aided jet system strikes and present two jet segmentation datasets, Libary and SegQinhu, with a view to advancing the study of water jet trajectory extraction in complex scenarios, as well as its application in jet-based operations. At the current stage of intelligent jet systems, jet extraction segmentation is one of the key technologies for achieving precise control and targeting. The primary objective of jet segmentation is to ensure that the water jet, whether in firefighting, marine operations, or underwater vehicles, accurately reaches its target--be it a fire source, a designated area, or a moving object. Accurate jet extraction and prediction can significantly improve the precision of jet-based operations, allowing the water jet to reliably reach the target area along a predetermined path, thus increasing operational efficiency and minimizing the impact on the surrounding environment. However, existing general segmentation methods often perform poorly in jet segmentation tasks. Traditional segmentation algorithms typically rely on the overall structure of the image and large-area target features, but these approaches face several challenges when dealing with jets. First, the proportion of the jet relative to the entire image is small, making it difficult for general algorithms to accurately extract subtle jet features from complex backgrounds. Second, the dynamic changes and irregular shapes of jets present additional challenges to traditional segmentation algorithms, resulting in segmentation outcomes that struggle to maintain continuity and stability. To address these issues, we propose benchmark datasets specifically for jet trajectory extraction and an improved MobileNetV2 method. Through extensive experimentation, we validated that depthwise separable convolutions in the MobileNetV2 network effectively reduce parameters and accelerate inference time compared to standard convolutions. Moreover, combining depthwise separable convolutions with residual networks significantly enhances network efficiency, making it more suitable for our proposed jet datasets. Hence, we selected the lightweight MobileNetV2 network as the baseline and improved it, achieving significant segmentation accuracy gains with minimal additional parameters. We envision that our open-access datasets will enable researchers to develop more robust and data-efficient algorithms for jet trajectory extraction and other related problems.