【摘要】 为解决部分遥感建筑物因为自身形状的不规则，导致传统矩形识别框算法对该类检测目标分割效果差，难以精确定位的问题，提出一种改进的Mask RCNN检测算法。改进Mask RCNN的主干网络FPN网络，简化特征融合过程，有效避免语义信息丢失；改进Mask RCNN的RPN网络，针对识别框的重复计算，提升其运算效率，提高检出率；调节mask掩膜参数，提高分割效果。实验结果表明，改进Mask RCNN目标检测算法的检测精度和召回率达到了99.80%和97.88%,较原算法分别提高了1.54%和1.65%,有效优化了遥感领域不规则建筑物的检测问题。更多还原

【Abstract】 To solve the problems that the traditional rectangular recognition box algorithm has poor segmentation effect on this kind of detection target and it is difficult to accurately locate due to the irregular shape of some remote sensing buildings, an improved Mask RCNN detection algorithm was proposed. The backbone network FPN of Mask RCNN was improved to simplify the process of feature fusion and effectively avoid the loss of semantic information. The RPN network of Mask RCNN was improved to improve its operation efficiency and detection rate for the repeated calculation of the recognition box. The mask parameters were adjusted to improve the segmentation effect. Experimental results show that the detection accuracy and recall rate of the improved Mask RCNN target detection algorithm reach 99.80% and 97.88%, which are 1.54% and 1.65% higher than that of the original algorithm, respectively. The detection problem of irregular buildings in remote sensing field is effectively optimized.更多还原