FastICENet: A real-time and accurate semantic segmentation model for aerial remote sensing river ice image

Zhang, Xiuwei; Zhao, Zixu; Ran, Lingyan; Xing, Yinghui; Wang, Wenna; Lan, Zeze; Yin, Hanlin; He, Hui; Liu, Qixing; Zhang, Baosen; Zhang, Yanning

doi:10.1016/j.sigpro.2023.109150

Cited by 4 publications

(3 citation statements)

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“…However, only coarse-grained river ice changes can be captured due to the long image-shooting interval. The second is unmanned aerial vehicle imaging (UAV imagery) [7][8][9], which has the advantage of its relatively low cost and ability to capture hourly data for river ice changes occurring anywhere. However, it is challenging to capture the long-term data for river ice changes.…”

Section: Introductionmentioning

confidence: 99%

“…A shortcoming was the inability to monitor the river ice for a long time by UAV. Zhang et al (2020Zhang et al ( -2023 [8,14,15] conducted a series of studies on the semantic segmentation of Previous studies have provided some methods for river ice regime recognition. Related to this paper, Daigle et al (2013) [13] used an artificial neural network and a particle image velocimetry method to measure the concentration and velocity of river ice on a camera, and this was a relatively early and comprehensive study on river ice recognition.…”

Section: Introductionmentioning

confidence: 99%

“…A shortcoming was the inability to monitor the river ice for a long time by UAV. Zhang et al (2020Zhang et al ( -2023 [8,14,15] conducted a series of studies on the semantic segmentation of river ice on oblique UAV images. Finally, they achieved real-time semantic segmentation while ensuring high accuracy, and they further calculated and analyzed the concentration of the river ice.…”

Section: Introductionmentioning

confidence: 99%

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River Ice Regime Recognition Based on Deep Learning: Ice Concentration, Area, and Velocity

Yang,

Zong,

Zhu

et al. 2023

Water

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The real-time derivation of the concentration, area, and velocity of river surface ice based on camera imagery is essential for predicting the potential risks related to ice blockages in water routes. The key lies in the continuous tracking and velocity measuring of river ice, and reliable ice motion detection is a prerequisite for the dynamic perception of tracking targets. Previous studies did not utilize motion tracking for measuring ice velocity, and particle image velocimetry and feature point matching were used. This study aimed to use deep learning methods to address the challenging problems of deriving the ice concentration, area, and velocity based on camera imagery, and the focus was on measuring the ice velocity and drawing trajectories using the particle video tracking algorithm. We built a dataset named IPC_RI_IDS and collected information during the ice cover break-up process in the Nenjiang River (China). Our suggested approach was divided into four steps: (1) image preprocessing, where the camera image was calibrated to real-world coordinates; (2) determining the ice and water pixels in the camera image using the lightweight semantic segmentation network and then calculating the ice concentration and area; (3) enhancing and optimizing motion detection using the semantic segmentation results; and (4) adapting the particle video tracking algorithm to measure ice velocity using the proposed tracking points generation strategy. Finally, we analyzed the surface ice data in the study area and attempted to predict the stage of the ice break-up process to provide support for the real-time short-term forecasts of ice floods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…A shortcoming was the inability to monitor the river ice for a long time by UAV. Zhang et al (2020Zhang et al ( -2023 [8,14,15] conducted a series of studies on the semantic segmentation of river ice on oblique UAV images. Finally, they achieved real-time semantic segmentation while ensuring high accuracy, and they further calculated and analyzed the concentration of the river ice.…”

Section: Introductionmentioning

confidence: 99%

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