计算机工程与应用2019，Vol.55Issue(21)：166-175,10.DOI:10.3778/j.issn.1002-8331.1903-0441
改进Mask R-CNN在航拍灾害检测的应用研究
Application Research of Improved Mask R-CNN in Aerial Disaster Detection
摘要
Abstract
Target detection has extremely high theoretical significance and application value in many fields. More stable and more accurate target detection methods have the hotspots and difficulties in the field of disaster detection. The target detection method based on deep learning is applied to disaster detection, and an aerial disaster detection method based on improved Mask R-CNN is proposed. In view of the low accuracy rate in the detection, the structure of the improved feature pyramid is used, and the information of the feature map is fully utilized to improve the detection accuracy of disaster targets of various sizes. And the online difficult sample excavator system is introduced to solve the problem of imbalance between positive and negative samples. At the same time, the multi-component combination method is adopted to eliminate the false detection target. In order to verify method effectiveness, forest fires, landslides, debris flows, and seismic aerial images of different heights are selected for verification experiments on the Tensorflow deep learning framework. Experimental results show that the proposed method can achieve fast and accurate detection of different types of disasters, which also has certain reference significance for target recognition research based on other application backgrounds.关键词
深度学习/灾害检测/特征金字塔/在线困难样本挖掘/多部件结合Key words
deep learning/disaster detection/feature pyramid/online difficult sample mining/multi-part combination分类
信息技术与安全科学引用本文复制引用
李梁,董旭彬,赵清华..改进Mask R-CNN在航拍灾害检测的应用研究[J].计算机工程与应用,2019,55(21):166-175,10.基金项目
国家自然科学基金(No.61872261) （No.61872261）
山西省社科联重点课题研究项目(No.SSKLZDKT2017090) （No.SSKLZDKT2017090）
太原师范学院大学生创新创业训练项目(No.CXCY1839). （No.CXCY1839）
