【摘要】 针对尾矿库位置及边界难准确提取问题，在传统实例分割网络基础上，提出一个具有多任务分支结构的实例分割网络(Multi-Task-Branch Network, MTBNet),并利用国产GF-1数据进行唐山地区的尾矿库提取试验。结果表明，召回率为95.8%时，尾矿库的检测准确率可达78.8%。新方法进一步优化了尾矿库目标框和轮廓质量，增强了模型的特征学习能力，有效提升了尾矿库的实例分割精度，可为唐山地区尾矿库动态监测作支撑，辅助尾矿库开采或生态保护。更多还原

【Abstract】 Aiming at the difficulty of accurate extraction of tailing pond location and boundary, an instance segmentation network with Multi-Task Branch structure based on the traditional instance segmentation network was proposed. Meanwhile, well-trained MTBNet was used to detect tailing ponds from GF-1 data for the Tangshan area. This novel method could achieve the detection accuracy of 78.8% while keeping the recall rate in a high level(95.8%). The method further optimized the qualities of the bounding box and mask, and enhanced the feature learning ability and effectively improved the instance segmentation accuracy of tailings pond, and it could be used as a data basis for dynamic monitoring of tailing ponds in Tangshan area, assisting tailing pond mining and reducing environmental pollution.更多还原