文献类型：期刊文献

英文题名：Automatic recognition of oil industry facilities based on deep learning

作者：Zhang, Nannan[1]; Liu, Yang[1]; Zou, Liqun[1]; Zhao, Hang[1]; Dong, Wentong[1]; Zhou, Hongying[1]; Guo, Hongyan[1]; Huang, Miaofen[2]

机构：[1] Research Institute of Petroleum Exploration and Development, PetroChina, Beijing, 100080, China; [2] Faculty of Mathematics and Computer Science, Guangdong Ocean University, Guangdong, 524088, China

年份：2018

卷号：2018-July

起止页码：2519

外文期刊名：International Geoscience and Remote Sensing Symposium (IGARSS)

收录：EI(收录号：20191206669205)

语种：英文

外文摘要：Effectively monitoring the real-time position and status of oil facilities (mainly well-site) in oil field is very important for the safety production. Considering the low efficiency of traditional visual interpretation method and the high demands of preset feature for machine learning method, one of the object detection methods in Deep learning (YOLOv2) was introduced to recognize oil industry facilities automatically. After establishing the dataset of oil facility samples, 90 percent of samples are used for model training while 10 percent are for validating. Comparing with the results extracted by machine learning (Adaboost model based on Haar-like), YOLOv2 recognition results of oil facilities indicated that: Deep learning improve the recognition efficiency and accuracy of oil facilities. The accuracy can be as high as 92% while the error rate and omission rate can be maintained in a low level. At the same time, the constructed model was applied in an oilfield in eastern part of China, and the result shows that the model can identify most of the oilfield facilities correctly with only 4% omission rate, which is much lower comparing with manual interpretation. However, the 11% error rate, caused by insufficient sample types and sample quantities, is relatively high especially in city area. ? 2018 IEEE