西安石油大学学报（自然科学版）2024，Vol.39Issue(2)：128-142,15.DOI:10.3969/j.issn.1673-064X.2024.02.016
基于改进DeepLabV3+的引导式道路提取方法及在震源点位优化中的应用
Guided Road Extraction Method Based on Improved DeepLabV3+and Its Application in Optimization of Source Positions
摘要
Abstract
In order to solve the problem of missing or incorrect extraction in automatic recognition method,a guided road information ex-traction method is proposed to improve the correction efficiency.This method adds an additional input channel(4th channel)in three o-riginal input channels of DeepLabV3+,converting the four poles of the road into a two-dimensional Gaussian heat map as additional channel input to the network.Taking the poles as guidance signals makes the network suitable for guided road information extraction tasks.The parallel multi-branch modules are designed to extract contextual information and enhance the feature extraction capabilitiy of the network.A new composite loss function is formed by fusing the class equilibrium binary cross entropy and dice coefficient to train and alleviate the imbalance of positive and negative samples.The network in this paper was verified on the public Deepglobe dataset and a actual 3D dataset in southwest China.PA,IOU and F1 on the public Deepglobe dataset reached 82.29%,68.81%and 81.52%re-spectively;On the 3D dataset,PA,IOU,and F1 reached 89.05%,81.01%and 89.51%respectively.Practical applications show that this method can effectively improve the accuracy of road recognition,with a compliance rate of over 85%with the road,providing accu-rate information for the subsequent deployment of seismic source points.关键词
道路拾取/深度学习/DeepLabV3+/震源点布设Key words
road picking/deep learning/DeepLabV3+/deployment of source points分类
能源科技引用本文复制引用
曹凯奇,张凌浩,徐虹,吴蔚,文武,周航..基于改进DeepLabV3+的引导式道路提取方法及在震源点位优化中的应用[J].西安石油大学学报（自然科学版）,2024,39(2):128-142,15.基金项目
国家自然科学基金面上项目"基于频变信息的流体识别及流体可动性预测"(41774142) （41774142）
四川省重点研发项目"工业互联网安全与智能管理平台关键技术研究与应用"(2023YFG0112) （2023YFG0112）
四川省自然科学基金资助项目"基于超分辨感知方法的密集神经图像分割"(2022NSFSC0964) （2022NSFSC0964）
