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

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.

Key words: deep learning, disaster detection, feature pyramid, online difficult sample mining, multi-part combination