Dynamic ice processes can significantly affect various river characteristics such as hydraulics, sediment transport, water quality and morphology. River ice can also impede ship navigation and can induce flood hazard. Study of ice processes is thus crucial for understanding rivers in cold regions. These processes vary according to the four different phases of river ice development: formation, progression, recession and breakup. Monitoring and observation of river ice by remote sensing and close-range photogrammetry have recently attracted significant attention from river ice researchers, and the emergence of remotely piloted aircraft systems with onboard cameras has facilitated safe surveying of river ice. Despite all the developments in this field, fast and accurate data acquisition is still very demanding. One of the challenging tasks in data acquisition from aerial imagery is ice detection and classification. This study presents a novel algorithm called IceMaskNet for automatic river ice detection and characterization from aerial imagery. IceMaskNet utilizes an improved version of the Mask R-CNN, a novel Region-based Convolutional Neural Network with an additional mask. The presented deep learning algorithm is able to detect river ice from aerial imagery and characterize it as belonging to one of six different classes: broken ice, frazil slush, ice cover, open water, border ice, or frazil pan. The developed algorithm is tested using data collected on the Dauphin River, in Manitoba, Canada. Aerial photography from several sections of the river with various slopes and bend scales were used to train IceMaskNet to detect, classify and characterize river ice. The presented algorithm detected and classified river ice with average accuracies of 95% and 91%, respectively.

动态冰过程可以显着影响各种河流特征，例如水力学、泥沙输送、水质和形态。河冰还会阻碍船舶航行，并可能引发洪水灾害。因此，研究冰过程对于了解寒冷地区的河流至关重要。这些过程根据河冰发展的四个不同阶段而有所不同：形成、发展、衰退和破裂。通过遥感和近距离摄影测量对河冰的监测和观察最近引起了河冰研究人员的极大关注，带有机载摄像头的遥控飞机系统的出现促进了河冰的安全调查。尽管该领域取得了诸多进展，但对快速准确的数据采集的要求仍然很高。从航拍图像获取数据的一项具有挑战性的任务是冰检测和分类。这项研究提出了一种新的算法，称为IceMaskNet用于从航空图像中自动检测河冰和表征。IceMaskNet利用了 Mask R-CNN 的改进版本，这是一种带有附加掩码的新型基于区域的卷积神经网络。所提出的深度学习算法能够从航拍图像中检测河冰，并将其表征为属于六种不同类别之一：碎冰、碎冰、冰盖、开阔水域、边界冰或碎冰。使用在加拿大马尼托巴省多芬河上收集的数据对开发的算法进行了测试。使用来自具有不同坡度和弯曲比例的河流的几个部分的航拍来训练IceMaskNet检测、分类和表征河冰。所提出的算法检测和分类河冰的平均准确率分别为 95% 和 91%。