Currently, instance segmentation is attracting more and more attention in machine learning region. However, there exists some defects on the information propagation in previous Mask R-CNN and other network models. In this paper, we propose supervised adaptive threshold network for instance segmentation. Specifically, we adopt the Mask R-CNN method based on adaptive threshold, and by establishing a layered adaptive network structure, it performs adaptive binarization on the probability graph generated by Mask RCNN to obtain better segmentation effect and reduce the error rate. At the same time, an adaptive feature pool is designed to make the transmission between different layers of the network more accurate and effective, reduce the loss in the process of feature transmission, and further improve the mask method. Experiments on benchmark data sets indicate that the effectiveness of the proposed model

翻译：目前,在机器学习区域中,分化案例正在吸引越来越多的注意力,然而,在以前的Mask R-CNN和其他网络模型中,在信息传播方面存在一些缺陷。在本文件中,我们提议有监督的适应阈值网络,例如分化。具体地说,我们采用基于适应阈值的Maske R-CNN方法,并通过建立一个分层的适应性网络结构,对Mask RCNNN生成的概率图进行适应性二进制,以获得更好的分化效果并减少误差率。与此同时,设计了一个适应性地物集合,以使网络不同层次之间的传输更加准确和有效,减少地物传输过程中的损失,并进一步改进掩码方法。关于基准数据集的实验表明,拟议模型的有效性。