水利水电技术（中英文）2024，Vol.55Issue(4)：163-175,13.DOI:10.13928/j.cnki.wrahe.2024.04.015
基于改进DeeplabV3+的水面多类型漂浮物分割方法研究
Research on segmentation method of multiple types of floating objects on water surface based on improved DeeplabV3+
摘要
Abstract
[Objective]In order to solve the problems of poor robustness of traditional image processing method and the inability of commonly used deep learning detection method to accurately recognize the boundaries of large floating objects,[Methods]a se-mantic segmentation method based on the improvement of DeeplabV3+for the recognition of multiple types of water surface floats was proposed,which improves the recognition ability of water surface floats.By classifying the collected actual water surface floats into categories,a homemade dataset is used for comparison experiments.The algorithm selects the xception network as the backbone network to obtain preliminary float features,introduces the attention mechanism in the enhanced feature extraction net-work part to emphasize the effective feature information,and incorporates the fully-connected conditional random field model in the post-processing stage to fuse the local information of a single pixel with the global semantic information.[Results]Comparing the image segmentation performance metrics,the improved algorithm mPA(Mean Pixel Accuracy)is improved by 5.73％and mIOU(Mean Intersection Over Union)is improved by 4.37％.[Conclusion]Compared with other algorithmic models,the im-proved DeeplabV3+algorithm is more capable of acquiring features of floating objects,and at the same time obtains rich detail in-formation to more accurately identify the boundaries of multiple types of water surface floating objects and more difficult to clas-sify floating objects,which meets the needs of floating object detection in the actual water environment after testing on multi-ple reservoir scenarios.关键词
深度学习/语义分割/特征提取/漂浮物识别/注意力机制/全连接条件随机场/算法模型/影响因素Key words
deep learning/semantic segmentation/feature extraction/floating object identification/attention mechanism/full connection condition random field/algorithmic model/influence factors分类
信息技术与安全科学引用本文复制引用
包学才,刘飞燕,聂菊根,许小华,柯华盛..基于改进DeeplabV3+的水面多类型漂浮物分割方法研究[J].水利水电技术（中英文）,2024,55(4):163-175,13.基金项目
国家自然科学基金项目(61961026) （61961026）
江西省水利厅科技项目(202223YBKT19) （202223YBKT19）
江西省科技厅重大科技研发专项"揭榜挂帅"制项目(20213AAG01012) （20213AAG01012）
