水利学报2026，Vol.57Issue(4)：524-534,11.DOI:10.3724/j.slxb.20250413
低对比度场景下冰水特征与冰封率智能识别研究
Research on intelligent recognition algorithms for ice-water features and ice concentration in low-contrast scenarios
摘要
Abstract
Ice hazards are prevalent in rivers,canals,and reservoirs in cold regions.As an important parameter influencing water heat loss and ice formation,ice concentration is used for evaluating ice hazards.Its efficient moni-toring and accurate identification are crucial for preventing ice floods.Compared with natural rivers,water convey-ance projects exhibit less variation in boundary and hydrodynamic conditions,as well as superior water quality.This makes it challenging to distinguish between ice and water,leading to greater errors in image-based ice concentration recognition methods.To address this challenge,an intelligent ice concentration recognition algorithm based on a deformable convolutional neural network is proposed.This algorithm incorporates deformable convolutional layers,which can adaptively adjust the sampling positions of convolutional kernels to achieve more accurate capture of com-plex ice and water features in low-contrast scenarios.A floating ice dataset containing 330 images from the Middle Route of South-to-North Water Diversion Project was constructed,and a five-fold cross-validation method was used to optimize the algorithm parameters.Experimental results of 16 typical floating ice images show that the average accuracy(ACC)of ice concentration identification reaches 0.96,while the mean intersection over union(IoU)achieves 0.91.Compared with commonly used ice concentration recognition algorithms such as the Otsu and SVM,the proposed algorithm improves the average ACC by 16％and 10％,and the average IoU by 19％and 9％,respec-tively.The findings of this study provide an alternative method for ice concentration recognition in water conveyance channels.关键词
低对比度/冰封率/智能识别/可变形卷积神经网络/南水北调中线工程Key words
low contrast/ice concentration/intelligent recognition/deformable convolutional neural network/the Middle Route of South-to-North Water Diversion Project分类
建筑与水利引用本文复制引用
李忠林,付辉,郭新蕾,陈晓楠,王军,夏庆福..低对比度场景下冰水特征与冰封率智能识别研究[J].水利学报,2026,57(4):524-534,11.基金项目
国家重点研发计划项目(2022YFC3202500) （2022YFC3202500）
国家自然科学基金项目(U2443221,52509118) （U2443221,52509118）
中国水科院科研专项项目(HY0145B032021) （HY0145B032021）
