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目标检测方法通常假定所有的误分类代价是相同的,期望获得较低的分类错误率,然而由于代价非均衡性和目标信息非充分性,这个假定在很多现实目标检测中并不适用.不平衡的误分类代价和非充分信息可能引起较高的误分类代价.为解决这个问题,文章提出了一种基于多粒度序贯三支决策的代价敏感目标检测算法.同时考虑多粒度特征,依据最小化误分类风险形成序贯检测策略.在每一步的序贯检测中,优化误分类代价,在目标信息不充分时做出延迟决策.构建了由粗粒度向细粒度转化的目标检测方法,可作出更合理的序贯检测决策.在多个目标检测数据集中的实验验证了代价敏感目标检测方法的有效性.“,”Many studies on object detection attempt to achieve a low misclassification error and they assume the misclassification costs are the same.Such assumption is unreasonable in many real-world applications due to the different costs and insufficient object information.Imbalanced misclassification costs and insuffi-cient information may lead to higher cost.To solve the issue,we propose a cost-sensitive multi-granularity sequential three-way decision method for Object Detection.The proposed method is based on sequential three-way decision(3WD)considering multi-granularity features.It develops a decision strategy which can minimize the total cost in the detection process.In each step,it optimizes the misclassification cost and makes delayed decision if the object information is insufficient.In the method,the object information con-verts from rough granularity to precise granularity in object detection and it may reach more reasonable de-cision.The experiments on several object detection databases are conducted to validate the effectiveness of the proposed method.